Tootfinder

Continuous Bangla Sign Language Translation: Mitigating the Expense of Gloss Annotation with the Assistance of Graph
Safaeid Hossain Arib, Rabeya Akter, Sejuti Rahman
https://arxiv.org/abs/2508.10687

Continuous Bangla Sign Language Translation: Mitigating the Expense of Gloss Annotation with the Assistance of Graph
Millions of individuals worldwide are affected by deafness and hearing impairment. Sign language serves as a sophisticated means of communication for the deaf and hard of hearing. However, in societies that prioritize spoken languages, sign language often faces underestimation, leading to communication barriers and social exclusion. The Continuous Bangla Sign Language Translation project aims to address this gap by enhancing translation methods. While recent approaches leverage transformer arch…

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
Abdul Haque, Umm e Hani, Ahmad Din, Muhammad Babar, Ali Abbas, Insaf Ullah
https://arxiv.org/abs/2506.11600

GraphRAG-Causal: A novel graph-augmented framework for causal reasoning and annotation in news
GraphRAG-Causal introduces an innovative framework that combines graph-based retrieval with large language models to enhance causal reasoning in news analysis. Traditional NLP approaches often struggle with identifying complex, implicit causal links, especially in low-data scenarios. Our approach addresses these challenges by transforming annotated news headlines into structured causal knowledge graphs. It then employs a hybrid retrieval system that merges semantic embeddings with graph-based s…

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae
https://arxiv.org/abs/2508.08748

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and texture. These factors complicate trajectory planning and grasping. This paper introduces a perception-action pipeline leveraging annotation-guided visual prompting, where bounding box annotations identify both pickable objects and placement locations, providing structured spatial guidance. Instead of traditional ste…

Motive-level Analysis of Form-functions Association in Korean Folk song
Danbinaerin Han, Dasaem Jeong, Juhan Nam
https://arxiv.org/abs/2508.10472 https://a…

Motive-level Analysis of Form-functions Association in Korean Folk song
Computational analysis of folk song audio is challenging due to structural irregularities and the need for manual annotation. We propose a method for automatic motive segmentation in Korean folk songs by fine-tuning a speech transcription model on audio lyric with motif boundary annotation. Applying this to 856 songs, we extracted motif count and duration entropy as structural features. Statistical analysis revealed that these features vary systematically according to the social function of the…

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges
Zhiqiang Yang, Renshuai Tao, Xiaolong Zheng, Guodong Yang, Chunjie Zhang
https://arxiv.org/abs/2508.09022

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges
Existing deepfake detection methods heavily depend on labeled training data. However, as AI-generated content becomes increasingly realistic, even \textbf{human annotators struggle to distinguish} between deepfakes and authentic images. This makes the labeling process both time-consuming and less reliable. Specifically, there is a growing demand for approaches that can effectively utilize large-scale unlabeled data from online social networks. Unlike typical unsupervised learning tasks, where c…

Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
Malik Khadar, Daniel Runningen, Julia Tang, Stevie Chancellor, Harmanpreet Kaur
https://arxiv.org/abs/2508.09911

Wisdom of the Crowd, Without the Crowd: A Socratic LLM for Asynchronous Deliberation on Perspectivist Data
Data annotation underpins the success of modern AI, but the aggregation of crowd-collected datasets can harm the preservation of diverse perspectives in data. Difficult and ambiguous tasks cannot easily be collapsed into unitary labels. Prior work has shown that deliberation and discussion improve data quality and preserve diverse perspectives -- however, synchronous deliberation through crowdsourcing platforms is time-intensive and costly. In this work, we create a Socratic dialog system using…

Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
Linh Nguyen, Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
https://arxiv.org/abs/2508.09832

Exploring the Potential of Large Language Models in Fine-Grained Review Comment Classification
Code review is a crucial practice in software development. As code review nowadays is lightweight, various issues can be identified, and sometimes, they can be trivial. Research has investigated automated approaches to classify review comments to gauge the effectiveness of code reviews. However, previous studies have primarily relied on supervised machine learning, which requires extensive manual annotation to train the models effectively. To address this limitation, we explore the potential of…

Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices
Jay L. Cunningham, Kevin Zhongyang Shao, Rock Yuren Pang, Nathaniel Mengist
https://arxiv.org/abs/2508.10071 …

Advancing Data Equity: Practitioner Responsibility and Accountability in NLP Data Practices
While research has focused on surfacing and auditing algorithmic bias to ensure equitable AI development, less is known about how NLP practitioners - those directly involved in dataset development, annotation, and deployment - perceive and navigate issues of NLP data equity. This study is among the first to center practitioners' perspectives, linking their experiences to a multi-scalar AI governance framework and advancing participatory recommendations that bridge technical, policy, and communi…

LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation
Fei Teng, Haoyang Li, Lei Chen
https://arxiv.org/abs/2508.09594 https://arxi…

LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation
Modern computing systems, such as HDFS and Spark, produce vast quantities of logs that developers use for tasks like anomaly detection and error analysis. To simplify log analysis, template generation methods have been proposed to standardize log formats, transforming unstructured data into structured templates. Existing heuristic-based methods and neural network-based methods suffer from low accuracy problems due to the reliance on handcrafted heuristics or specific log patterns in training se…

Data-Efficient Learning for Generalizable Surgical Video Understanding
Sahar Nasirihaghighi
https://arxiv.org/abs/2508.10215 https://arxiv.org/pdf/2508.102…

Data-Efficient Learning for Generalizable Surgical Video Understanding
Advances in surgical video analysis are transforming operating rooms into intelligent, data-driven environments. Computer-assisted systems support full surgical workflow, from preoperative planning to intraoperative guidance and postoperative assessment. However, developing robust and generalizable models for surgical video understanding remains challenging due to (I) annotation scarcity, (II) spatiotemporal complexity, and (III) domain gap across procedures and institutions. This doctoral rese…

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Haoran Sun, Haoyu Bian, Shaoning Zeng, Yunbo Rao, Xu Xu, Lin Mei, Jianping Gou
https://arxiv.org/abs/2507.08648

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Common knowledge indicates that the process of constructing image datasets usually depends on the time-intensive and inefficient method of manual collection and annotation. Large models offer a solution via data generation. Nonetheless, real-world data are obviously more valuable comparing to artificially intelligence generated data, particularly in constructing image datasets. For this reason, we propose a novel method for auto-constructing datasets from real-world images by a multiagent colla…

This https://arxiv.org/abs/2411.14464 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data
Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low. Results: We introduce in this paper a novel paradigm (JESTR) for annotation. Unlike prior approaches that explicitly construct molecular fingerprints or spectra, JESTR leverages the insight that molecules and their corresponding spec…

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
Mahdi Dhaini, Juraj Vladika, Ege Erdogan, Zineb Attaoui, Gjergji Kasneci
https://arxiv.org/abs/2508.09776

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and enriching datasets with interpretable labels. Traditional approaches rely on human annotation, which is costly, labor-intensive, and impedes scalability. In this work, we present an automated framework that leverages multiple state-of-the-art large language models (LLMs) to generate high-quality textual explanations. …

Probably Approximately Correct Labels
Emmanuel J. Cand\`es, Andrew Ilyas, Tijana Zrnic
https://arxiv.org/abs/2506.10908 https://arxiv…

Probably Approximately Correct Labels
Obtaining high-quality labeled datasets is often costly, requiring either extensive human annotation or expensive experiments. We propose a method that supplements such "expert" labels with AI predictions from pre-trained models to construct labeled datasets more cost-effectively. Our approach results in probably approximately correct labels: with high probability, the overall labeling error is small. This solution enables rigorous yet efficient dataset curation using modern AI models. We demon…

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Zixuan Jiang, Renjing Xu
https://arxiv.org/abs/2506.07035 https://

AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimization
Deciphering protein function remains a fundamental challenge in protein representation learning. The task presents significant difficulties for protein language models (PLMs) due to the sheer volume of functional annotation categories and the highly imbalanced distribution of annotated instances across biological ontologies. Inspired by the remarkable success of reinforcement learning from human feedback (RLHF) in large language model (LLM) alignment, we propose AnnoDPO, a novel multi-modal fra…

This https://arxiv.org/abs/2502.07404 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the psychological and practical factors critical to successful collaboration. Specifically, we investigate…

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Zhetao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
https://arxiv.org/abs/2507.06670

STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation
Recent breakthroughs in singing voice synthesis (SVS) have heightened the demand for high-quality annotated datasets, yet manual annotation remains prohibitively labor-intensive and resource-intensive. Existing automatic singing annotation (ASA) methods, however, primarily tackle isolated aspects of the annotation pipeline. To address this fundamental challenge, we present STARS, which is, to our knowledge, the first unified framework that simultaneously addresses singing transcription, alignme…

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Haoyang Li, Jiaqing Li, Jialun Cao, Zongyuan Yang, Yongping Xiong
https://arxiv.org/abs/2508.09220 …

Towards Scalable Training for Handwritten Mathematical Expression Recognition
Large foundation models have achieved significant performance gains through scalable training on massive datasets. However, the field of \textbf{H}andwritten \textbf{M}athematical \textbf{E}xpression \textbf{R}ecognition (HMER) has been impeded by the scarcity of data, primarily due to the arduous and costly process of manual annotation. To bridge this gap, we propose a novel method integrating limited handwritten formulas with large-scale LaTeX-rendered formulas by developing a scalable data e…

ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
Samiha Afaf Neha, Abir Ahammed Bhuiyan, Md. Ishrak Khan
https://arxiv.org/abs/2508.07345

ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
\textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing …

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Chiyu Zhang, Lu Zhou, Xiaogang Xu, Jiafei Wu, Liming Fang, Zhe Liu
https://arxiv.org/abs/2508.10390 https:…

Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Evaluating jailbreak attacks is challenging when prompts are not overtly harmful or fail to induce harmful outputs. Unfortunately, many existing red-teaming datasets contain such unsuitable prompts. To evaluate attacks accurately, these datasets need to be assessed and cleaned for maliciousness. However, existing malicious content detection methods rely on either manual annotation, which is labor-intensive, or large language models (LLMs), which have inconsistent accuracy in harmful types. To b…

This https://arxiv.org/abs/2502.18744 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose ZEBRA - a model behavior-wise zero-annotation framework that constructs preference data by leveraging model behavior knowledge derived from benchmark performances. ZEBRA binarizes response pairs by …

Just released version 0.10.0 of the Chessboard Viewer Obsidian plugin. This version includes the ability to annotate squares with icons.

Automated Type Annotation in Python Using Large Language Models
Varun Bharti, Shashwat Jha, Dhruv Kumar, Pankaj Jalote
https://arxiv.org/abs/2508.00422 https://

Automated Type Annotation in Python Using Large Language Models
Type annotations in Python enhance maintainability and error detection. However, generating these annotations manually is error prone and requires extra effort. Traditional automation approaches like static analysis, machine learning, and deep learning struggle with limited type vocabularies, behavioral over approximation, and reliance on large labeled datasets. In this work, we explore the use of LLMs for generating type annotations in Python. We develop a generate check repair pipeline: the L…

EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesis
Jianwu Fang, Lei-Lei Li, Zhedong Zheng, Hongkai Yu, Jianru Xue, Zhengguo Li, Tat-Seng Chua
https://arxiv.org/abs/2506.10002

EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesis
Traffic Accident Anticipation (TAA) in traffic scenes is a challenging problem for achieving zero fatalities in the future. Current approaches typically treat TAA as a supervised learning task needing the laborious annotation of accident occurrence duration. However, the inherent long-tailed, uncertain, and fast-evolving nature of traffic scenes has the problem that real causal parts of accidents are difficult to identify and are easily dominated by data bias, resulting in a background confound…

miRKatAI: An Integrated Database and Multi-agent AI system for microRNA Research
Karen Guerrero-Vazquez, Jacopo Umberto Verga, Pilib O Broin, Katarzyna Goljanek-Whysall
https://arxiv.org/abs/2508.08331

miRKatAI: An Integrated Database and Multi-agent AI system for microRNA Research
MicroRNAs (miRs) are robust regulators of gene expression, implicated in most biological processes. microRNAs predominantly downregulate the expression of genes post-transcriptionally and each miR is predicted to target several hundred genes. The accurate identification and annotation of miR-mRNA target interactions is central to understanding miRs function and their therapeutic potential. However, computational target prediction is challenging due to imperfect complementarity of miRs with thei…

Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
Yueyi Wang, Qiyao Wei
https://arxiv.org/abs/2508.07408

Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use an LLM to automatically assign multi-label event categories to high-sentiment-intensity tweets. We align these labeled sentiment signals with forward returns over 1-to-7-day horizons to evaluate their statistical efficacy and market tradability. Our experiments reveal that certain event labels con…

scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Olga Ovcharenko, Florian Barkmann, Philip Toma, Imant Daunhawer, Julia Vogt, Sebastian Schelter, Valentina Boeva
https://arxiv.org/abs/2506.10031

scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data
Self-supervised learning (SSL) has proven to be a powerful approach for extracting biologically meaningful representations from single-cell data. To advance our understanding of SSL methods applied to single-cell data, we present scSSL-Bench, a comprehensive benchmark that evaluates nineteen SSL methods. Our evaluation spans nine datasets and focuses on three common downstream tasks: batch correction, cell type annotation, and missing modality prediction. Furthermore, we systematically assess v…

The Cell Ontology in the age of single-cell omics
Shawn Zheng Kai Tan, Aleix Puig-Barbe, Damien Goutte-Gattat, Caroline Eastwood, Brian Aevermann, Alida Avola, James P Balhoff, Ismail Ugur Bayindir, Jasmine Belfiore, Anita Reane Caron, David S Fischer, Nancy George, Benjamin M Gyori, Melissa A Haendel, Charles Tapley Hoyt, Huseyin Kir, Tiago Lubiana, Nicolas Matentzoglu, James A Overton, Beverly Peng, Bjoern Peters, Ellen M Quardokus, Patrick L Ray, Paola Roncaglia, Andrea D Rivera, Ra…

The Cell Ontology in the age of single-cell omics
Single-cell omics technologies have transformed our understanding of cellular diversity by enabling high-resolution profiling of individual cells. However, the unprecedented scale and heterogeneity of these datasets demand robust frameworks for data integration and annotation. The Cell Ontology (CL) has emerged as a pivotal resource for achieving FAIR (Findable, Accessible, Interoperable, and Reusable) data principles by providing standardized, species-agnostic terms for canonical cell types - …

From Coarse to Fine-Grained Emotion Annotation: An Immediate Recall Paradigm with Validation through Physiological Evidence and Recognition Performance
Hao Tang, Songyun Xie, Xinzhou Xie, Can Liao, Xin Zhang, Bohan Li, Zhongyu Tian, Dalu Zheng
https://arxiv.org/abs/2507.02350

From Coarse to Fine-Grained Emotion Annotation: An Immediate Recall Paradigm with Validation through Physiological Evidence and Recognition Performance
Traditional video-induced emotion physiological datasets often use whole-trial annotation, assigning a single emotion label to all data collected during an entire trial. This coarse-grained annotation approach misaligns with the dynamic and temporally localized nature of emotional responses as they unfold with video narratives, introducing label noise that limits emotion recognition algorithm evaluation and performance. To solve the label noise problem caused by coarse-grained annotation, we pr…

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
toXiv_bot_toot

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Efficient Annotation of Medieval Charters
Anguelos Nicolaou, Daniel Luger, Franziska Decker, Nicolas Renet, Vincent Christlein, Georg Vogeler

I feel a while ago I saw website here where you could enter an #openstreetmap building ID and it would make a 3D render based on the 3D annotation. For the life of me I can't find the URL, does anyone have an idea?
@… I think you might have shared it at some point with the bandstand of Greenwich Park 😄
[EDIT: Answered thanks to @… , see options here https://floss.social/@hbond/114965004674098103 ]

Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Yingyu Yang, Qianye Yang, Kangning Cui, Can Peng, Elena D'Alberti, Netzahualcoyotl Hernandez-Cruz, Olga Patey, Aris T. Papageorghiou, J. Alison Noble
https://arxiv.org/abs/2507.05154…

Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detection, typically require extensive annotations, which is time-consuming and labor-intensive. In this paper, we present an unsupervised framework for end-diastole (ED) and end-systole (ES) detection through self-supervised learning of latent cardiac motion trajectories from 4-chamber-view echocardiography videos. Our meth…

Scalable Controllable Accented TTS
Henry Li Xinyuan, Zexin Cai, Ashi Garg, Kevin Duh, Leibny Paola Garc\'ia-Perera, Sanjeev Khudanpur, Nicholas Andrews, Matthew Wiesner
https://arxiv.org/abs/2508.07426

Scalable Controllable Accented TTS
We tackle the challenge of scaling accented TTS systems, expanding their capabilities to include much larger amounts of training data and a wider variety of accent labels, even for accents that are poorly represented or unlabeled in traditional TTS datasets. To achieve this, we employ two strategies: 1. Accent label discovery via a speech geolocation model, which automatically infers accent labels from raw speech data without relying solely on human annotation; 2. Timbre augmentation through kN…

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Chihiro Yamasaki, Kai Sugahara, Yuma Nagi, Kazushi Okamoto
https://arxiv.org/abs/2507.03945

Function-based Labels for Complementary Recommendation: Definition, Annotation, and LLM-as-a-Judge
Complementary recommendations enhance the user experience by suggesting items that are frequently purchased together while serving different functions from the query item. Inferring or evaluating whether two items have a complementary relationship requires complementary relationship labels; however, defining these labels is challenging because of the inherent ambiguity of such relationships. Complementary labels based on user historical behavior logs attempt to capture these relationships, but …

A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
Shuying Huang, Junpeng Li, Changchun Hua, Yana Yang
https://arxiv.org/abs/2507.07771

A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
To alleviate the annotation burden in supervised learning, N-tuples learning has recently emerged as a powerful weakly-supervised method. While existing N-tuples learning approaches extend pairwise learning to higher-order comparisons and accommodate various real-world scenarios, they often rely on task-specific designs and lack a unified theoretical foundation. In this paper, we propose a general N-tuples learning framework based on empirical risk minimization, which systematically integrates …

I've just noticed that my railway pass no longer carries an explicit "valid (…) in any class" annotation. It still has an "X" in the "class" field, but that's not that cool.
(Explanation: many years ago, there used to be a few "RegioExpress" trains with first class seats. Of course, barely anybody cared about that, and I doubt people actually bought first class tickets. Alas, these are long gone.)
#rail

FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
Xusheng He, Wei Liu, Shanshan Ma, Qian Liu, Chenghao Ma, Jianlong Wu
https://arxiv.org/abs/2508.07554

FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding
Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advancements in general video understanding. This difficulty arises primarily from the scarcity of datasets with sufficiently rich and domain-specific annotations. To bridge this gap, we introduce FineBadminton, a novel and large-scale dataset featuring a unique multi-level semantic annotation hierarchy (Foundational Actions, …

{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
Miryeong Kwon, Donghyun Gouk, Junhyeok Jang, Jinwoo Baek, Hyunwoo You, Sangyoon Ji, Hongjoo Jung, Junseok Moon, Seungkwan Kang, Seungjun Lee, Myoungsoo Jung
https://arxiv.org/abs/2506.15613

From Block to Byte: Transforming PCIe SSDs with CXL Memory Protocol and Instruction Annotation
This paper explores how Compute Express Link (CXL) can transform PCIe-based block storage into a scalable, byte-addressable working memory. We address the challenges of adapting block storage to CXL's memory-centric model by emphasizing cacheability as a key enabler and advocating for Type 3 endpoint devices, referred to as CXL-SSDs. To validate our approach, we prototype a CXL-SSD on a custom FPGA platform and propose annotation mechanisms, Determinism and Bufferability, to enhance performance…

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
Hope Schroeder, Deb Roy, Jad Kabbara
https://arxiv.org/abs/2507.15821

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
LLM use in annotation is becoming widespread, and given LLMs' overall promising performance and speed, simply "reviewing" LLM annotations in interpretive tasks can be tempting. In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution, impacting both the evaluation of LLM performance, and analysis using these labels in a social science task downstream. We conducted a pre-registered experiment with 410 unique annotators and over 7,000…

Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
Chenfei Xiong, Jingwei Ni, Yu Fan, Vil\'em Zouhar, Donya Rooein, Lorena Calvo-Bartolom\'e, Alexander Hoyle, Zhijing Jin, Mrinmaya Sachan, Markus Leippold, Dirk Hovy, Mennatallah El-Assady, Elliott Ash
https://arxiv.org/abs/2507.05010

Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic annotation guided by large language models (LLMs). Co-DETECT starts with an initial, sketch-level codebook and dataset provided by a domain expert, then leverages the LLM to annotate the data and identify edge cases that are not well described by the initial codebook. Specifically, Co-DETECT flags challenging examples…

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang
https://arxiv.org/abs/2508.03441

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful f…

📚 Developed by #AnthropicFellows with #DecodeResearch collaboration
🛠️ Supports circuit tracing, visualization, annotation and sharing capabilities

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
Martin J. O'Connor, Marcos Martinez-Romero, Attila L. Egyedi, Mete U. Akdogan, Michael V. Dorf, Mark A. Musen
https://arxiv.org/abs/2508.00859

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
High-quality, "rich" metadata are essential for making research data findable, interoperable, and reusable. The Center for Expanded Data Annotation and Retrieval (CEDAR) has long addressed this need by providing tools to design machine-actionable metadata templates that encode community standards in a computable form. To make these capabilities more accessible within real-world research workflows, we have developed the CEDAR Embeddable Editor (CEE)-a lightweight, interoperable Web Component tha…

REFS: Robust EEG feature selection with missing multi-dimensional annotation for emotion recognition
Xueyuan Xu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05933

ProCaliper: functional and structural analysis, visualization, and annotation of proteins
Jordan C. Rozum, Hunter Ufford, Alexandria K. Im, Tong Zhang, David D. Pollock, Doo Nam Kim, Song Feng
https://arxiv.org/abs/2506.19961

ProCaliper: functional and structural analysis, visualization, and annotation of proteins
Understanding protein function at the molecular level requires connecting residue-level annotations with physical and structural properties. This can be cumbersome and error-prone when functional annotation, computation of physico-chemical properties, and structure visualization are separated. To address this, we introduce ProCaliper, an open-source Python library for computing and visualizing physico-chemical properties of proteins. It can retrieve annotation and structure data from UniProt an…

This https://arxiv.org/abs/2502.16517 has been replaced.
link: https://scholar.google.com/scholar?q=a

Annotation-guided AoS-to-SoA conversions and GPU offloading with data views in C++
The C++ programming language provides classes and structs as fundamental modeling entities. Consequently, C++ code tends to favour array-of-structs (AoS) for encoding data sequences, even though structure-of-arrays (SoA) yields better performance for some calculations. We propose a C++ language extension based on attributes that allows developers to guide the compiler in selecting memory arrangements, i.e.~to select the optimal choice between AoS and SoA dynamically depending on both the execut…

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Qibiao Wu, Yagang Wang, Qian Zhang
https://arxiv.org/abs/2507.06581 https://

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which oft…

Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications
Leila Tavakoli, Hamed Zamani
https://arxiv.org/abs/2507.00543

Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications
Despite growing interest in using large language models (LLMs) to automate annotation, their effectiveness in complex, nuanced, and multi-dimensional labelling tasks remains relatively underexplored. This study focuses on annotation for the search clarification task, leveraging a high-quality, multi-dimensional dataset that includes five distinct fine-grained annotation subtasks. Although LLMs have shown impressive capabilities in general settings, our study reveals that even state-of-the-art m…

@… Some language servers do this. The Haskell Language Server, for example, will let you add an explicit type annotation for an implicitly typed variable or function. I think I’ve seen this for TypeScript too.
I’m not sure if this answers your question, but I hope it does.

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
Grace Nansamba, Evelyn Namugwanya, David Boehme, Dewi Yokelson, Riley Shipley, Derek Schafer, Michael McKinsey, Olga Pearce, Anthony Skjellum
https://arxiv.org/abs/2507.22372

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
We introduce ``communication regions'' into the widely used Caliper HPC profiling tool. A communication region is an annotation enabling capture of metrics about the data being communicated (including statistics of these metrics), and metrics about the MPI processes involved in the communications, something not previously possible in Caliper. We explore the utility of communication regions with three representative modeling and simulation applications, AMG2023, Kripke, and Laghos, all part of t…

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park, Ryuichi Yamamoto
https://arxiv.org/abs/2506.04527

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the labels, the proposed model conditions the label generation on corresponding graphemes by two methods: 1) Add implicit grapheme conditioning through prompt encoder using pre-trained BERT features. 2) Explicitly prune the label hypotheses inconsistent with the grapheme during inference. These methods enable obtaining p…

ChartMark: A Structured Grammar for Chart Annotation
Yiyu Chen, Yifan Wu, Shuyu Shen, Yupeng Xie, Leixian Shen, Hui Xiong, Yuyu Luo
https://arxiv.org/abs/2507.21810 https://

ChartMark: A Structured Grammar for Chart Annotation
Chart annotations enhance visualization accessibility but suffer from fragmented, non-standardized representations that limit cross-platform reuse. We propose ChartMark, a structured grammar that separates annotation semantics from visualization implementations. ChartMark features a hierarchical framework mapping onto annotation dimensions (e.g., task, chart context), supporting both abstract intents and precise visual details. Our toolkit demonstrates converting ChartMark specifications into V…

RightTyper: Effective and Efficient Type Annotation for Python
Juan Altmayer Pizzorno, Emery D. Berger
https://arxiv.org/abs/2507.16051 https://

RightTyper: Effective and Efficient Type Annotation for Python
Python type annotations bring the benefits of static type checking to the language. However, manually writing annotations can be time-consuming and tedious. The result is that most real-world Python code remains largely untyped. Past approaches to annotating types in Python code fall short in a number of ways. Static approaches struggle with dynamic features and infer overly broad types. AI-based methods are inherently unsound and can miss rare or user-defined types. Dynamic methods can impose …

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
Jiachi Chen, Yiming Shen, Jiashuo Zhang, Zihao Li, John Grundy, Zhenzhe Shao, Yanlin Wang, Jiashui Wang, Ting Chen, Zibin Zheng
https://arxiv.org/abs/2506.18795

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitatio…

This https://arxiv.org/abs/2505.13556 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures
Accurate and timely alerts for drivers or automated systems to unfolding collisions remains a challenge in road safety, particularly in highly interactive urban traffic. Existing approaches require labour-intensive annotation of sparse risk, struggle to consider varying contextual factors, or are useful only in the scenarios they are designed for. To address these limits, this study introduces the generalised surrogate safety measure (GSSM), a new approach that learns exclusively from naturalis…

This https://arxiv.org/abs/2308.03734 has been replaced.
link: https://scholar.google.com/scholar?q=a

Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which…

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
Yu Zhang, Ruijie Yu, Jidong Tian, Feng Zhu, Jiapeng Liu, Xiaokang Yang, Yaohui Jin, Yanyan Xu
https://arxiv.org/abs/2506.23520

ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data
With the increasing interest in robotic synthesis in the context of organic chemistry, the automated extraction of chemical procedures from literature is critical. However, this task remains challenging due to the inherent ambiguity of chemical language and the high cost of human annotation required for developing reliable computer-aided extraction protocols. Here, we present ChemActor, a fully fine-tuned large language model (LLM), as a chemical executor to convert between unstructured experim…

SENSOR: An ML-Enhanced Online Annotation Tool to Uncover Privacy Concerns from User Reviews in Social-Media Applications
Labiba Farah, Mohammad Ridwan Kabir, Shohel Ahmed, MD Mohaymen Ul Anam, Md. Sakibul Islam
https://arxiv.org/abs/2507.10640

SENSOR: An ML-Enhanced Online Annotation Tool to Uncover Privacy Concerns from User Reviews in Social-Media Applications
The widespread use of social media applications has raised significant privacy concerns, often highlighted in user reviews. These reviews also provide developers with valuable insights into improving apps by addressing issues and introducing better features. However, the sheer volume and nuanced nature of reviews make manual identification and prioritization of privacy-related concerns challenging for developers. Previous studies have developed software utilities to automatically classify user …

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Ariana Sahitaj, Premtim Sahitaj, Veronika Solopova, Jiaao Li, Sebastian M\"oller, Vera Schmitt
https://arxiv.org/abs/2507.18343

Hybrid Annotation for Propaganda Detection: Integrating LLM Pre-Annotations with Human Intelligence
Propaganda detection on social media remains challenging due to task complexity and limited high-quality labeled data. This paper introduces a novel framework that combines human expertise with Large Language Model (LLM) assistance to improve both annotation consistency and scalability. We propose a hierarchical taxonomy that organizes 14 fine-grained propaganda techniques into three broader categories, conduct a human annotation study on the HQP dataset that reveals low inter-annotator agreeme…

Autoadaptive Medical Segment Anything Model
Tyler Ward, Meredith K. Owen, O'Kira Coleman, Brian Noehren, Abdullah-Al-Zubaer Imran
https://arxiv.org/abs/2507.01828

Autoadaptive Medical Segment Anything Model
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of labeled training data, typically obtained through manual annotation, which can be an expensive, time-consuming, and error-prone process. This signals a need for accurate, automatic, and annotation-efficient methods of training these models. We propose ADA-SAM (automated, domain-specific, and adaptive segment anything…

GRIT: Graph-Regularized Logit Refinement for Zero-shot Cell Type Annotation
Tianxiang Hu, Chenyi Zhou, Jiaxiang Liu, Jiongxin Wang, Ruizhe Chen, Haoxiang Xia, Gaoang Wang, Jian Wu, Zuozhu Liu
https://arxiv.org/abs/2508.04747

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Yahan Yu, Yuyang Dong, Masafumi Oyamada
https://arxiv.org/abs/2507.06999

Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs
Reasoning is a key capability for large language models (LLMs), particularly when applied to complex tasks such as mathematical problem solving. However, multimodal reasoning research still requires further exploration of modality alignment and training costs. Many of these approaches rely on additional data annotation and relevant rule-based rewards to enhance the understanding and reasoning ability, which significantly increases training costs and limits scalability. To address these challeng…

Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactions
Weiyan Shi, Kenny Tsu Wei Choo
https://arxiv.org/abs/2506.05879

Human-AI Alignment of Multimodal Large Language Models with Speech-Language Pathologists in Parent-Child Interactions
Joint attention is a critical marker of early social-communicative development, yet remains difficult for caregivers to assess without expert guidance. In this work, we explore how multimodal large language models (MLLMs) can be aligned with the reasoning processes of speech-language pathologists (SLPs) to support the interpretation of everyday parent-child interactions. We conducted in-depth interviews and video annotation studies with three experienced SLPs to uncover how they evaluate joint …

Spegion: Implicit and Non-Lexical Regions with Sized Allocations
Jack Hughes, Michael Vollmer, Mark Batty
https://arxiv.org/abs/2506.02182 https://

Spegion: Implicit and Non-Lexical Regions with Sized Allocations
Region based memory management is a powerful tool designed with the goal of ensuring memory safety statically. The region calculus of Tofte and Talpin is a well known example of a region based system, which uses regions to manage memory in a stack-like fashion. However, the region calculus is lexically scoped and requires explicit annotation of memory regions, which can be cumbersome for the programmer. Other systems have addressed non-lexical regions, but these approaches typically require the…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Zhiyuan Yang, Kai Li, Sophia Ghamoshi Ramandi, Patricia Brassard, Hakim Khellaf, Vincent Quoc-Huy Trinh, Jennifer Zhang, Lina Chen, Corwyn Rowsell, Sonal Varma, Kostas Plataniotis, Mahdi S. Hosseini
https://ar…

ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Computational pathology (CoPath) leverages histopathology images to enhance diagnostic precision and reproducibility in clinical pathology. However, publicly available datasets for CoPath that are annotated with extensive histological tissue type (HTT) taxonomies at a granular level remain scarce due to the significant expertise and high annotation costs required. Existing datasets, such as the Atlas of Digital Pathology (ADP), address this by offering diverse HTT annotations generalized to mul…

This https://arxiv.org/abs/2506.06155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

A Novel Large-scale Crop Dataset and Dual-stream Transformer Method for Fine-grained Hierarchical Crop Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series
Fine-grained crop classification is crucial for precision agriculture and food security monitoring. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these …

Melodic and Metrical Elements of Expressiveness in Hindustani Vocal Music
Yash Bhake, Ankit Anand, Preeti Rao
https://arxiv.org/abs/2508.04430 https://arxi…

Melodic and Metrical Elements of Expressiveness in Hindustani Vocal Music
This paper presents an attempt to study the aesthetics of North Indian Khayal music with reference to the flexibility exercised by artists in performing popular compositions. We study expressive timing and pitch variations of the given lyrical content within and across performances and propose computational representations that can discriminate between different performances of the same song in terms of expression. We present the necessary audio processing and annotation procedures, and discuss…

This https://arxiv.org/abs/2405.17492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

StatWhy: Formal Verification Tool for Statistical Hypothesis Testing Programs
Statistical methods have been widely misused and misinterpreted in various scientific fields, raising significant concerns about the integrity of scientific research. To mitigate this problem, we propose a tool-assisted method for formally specifying and automatically verifying the correctness of statistical programs. In this method, programmers are required to annotate the source code of the statistical programs with the requirements for these methods. Through this annotation, they are reminde…

Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis
Yifan Wang, Hongfeng Ai, Ruiqi Li, Maowei Jiang, Ruiyuan Kang, Jiahua Dong, Cheng Jiang, Chenzhong Li
https://arxiv.org/abs/2508.05572

Discrepancy-Aware Contrastive Adaptation in Medical Time Series Analysis
In medical time series disease diagnosis, two key challenges are identified. First, the high annotation cost of medical data leads to overfitting in models trained on label-limited, single-center datasets. To address this, we propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge, providing valuable references for downstream tasks. Second, many existing studies employ contrastive learning to derive more generalized medical sequence representation…

A Highly Clean Recipe Dataset with Ingredient States Annotation for State Probing Task
Mashiro Toyooka, Kiyoharu Aizawa, Yoko Yamakata
https://arxiv.org/abs/2507.17232 https://

A Highly Clean Recipe Dataset with Ingredient States Annotation for State Probing Task
Large Language Models (LLMs) are trained on a vast amount of procedural texts, but they do not directly observe real-world phenomena. In the context of cooking recipes, this poses a challenge, as intermediate states of ingredients are often omitted, making it difficult for models to track ingredient states and understand recipes accurately. In this paper, we apply state probing, a method for evaluating a language model's understanding of the world, to the domain of cooking. We propose a new tas…

R1-RE: Cross-Domain Relationship Extraction with RLVR
Runpeng Dai, Tong Zheng, Run Yang, Hongtu Zhu
https://arxiv.org/abs/2507.04642 https://

R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elic…

Sequential Attention-based Sampling for Histopathological Analysis
Tarun G, Naman Malpani, Gugan Thoppe, Sridharan Devarajan
https://arxiv.org/abs/2507.05077

Sequential Attention-based Sampling for Histopathological Analysis
Deep neural networks are increasingly applied for automated histopathology. Yet, whole-slide images (WSIs) are often acquired at gigapixel sizes, rendering it computationally infeasible to analyze them entirely at high resolution. Diagnostic labels are largely available only at the slide-level, because expert annotation of images at a finer (patch) level is both laborious and expensive. Moreover, regions with diagnostic information typically occupy only a small fraction of the WSI, making it in…

AnnoGram: An Annotative Grammar of Graphics Extension
Md Dilshadur Rahman, Md Rahat-uz- Zaman, Andrew McNutt, Paul Rosen
https://arxiv.org/abs/2507.04236 h…

AnnoGram: An Annotative Grammar of Graphics Extension
Annotations are central to effective data communication, yet most visualization tools treat them as secondary constructs -- manually defined, difficult to reuse, and loosely coupled to the underlying visualization grammar. We propose a declarative extension to Wilkinson's Grammar of Graphics that reifies annotations as first-class design elements, enabling structured specification of annotation targets, types, and positioning strategies. To demonstrate the utility of our approach, we develop a …

StepAL: Step-aware Active Learning for Cataract Surgical Videos
Nisarg A. Shah, Bardia Safaei, Shameema Sikder, S. Swaroop Vedula, Vishal M. Patel
https://arxiv.org/abs/2507.22059

StepAL: Step-aware Active Learning for Cataract Surgical Videos
Active learning (AL) can reduce annotation costs in surgical video analysis while maintaining model performance. However, traditional AL methods, developed for images or short video clips, are suboptimal for surgical step recognition due to inter-step dependencies within long, untrimmed surgical videos. These methods typically select individual frames or clips for labeling, which is ineffective for surgical videos where annotators require the context of the entire video for annotation. To addre…

This https://arxiv.org/abs/2407.17490 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents
AI agents have drawn increasing attention mostly on their ability to perceive environments, understand tasks, and autonomously achieve goals. To advance research on AI agents in mobile scenarios, we introduce the Android Multi-annotation EXpo (AMEX), a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents which are capable of completing tasks by directly interacting with the graphical user interface (GUI) on mobile devices. AMEX comprises over 104K high-resolution…

LLM-Based Repair of Static Nullability Errors
Nima Karimipour, Michael Pradel, Martin Kellogg, Manu Sridharan
https://arxiv.org/abs/2507.20674 https://arxi…

LLM-Based Repair of Static Nullability Errors
Modern Java projects increasingly adopt static analysis tools that prevent null-pointer exceptions by treating nullness as a type property. However, integrating such tools into large, existing codebases remains a significant challenge. While annotation inference can eliminate many errors automatically, a subset of residual errors -- typically a mix of real bugs and false positives -- often persist and can only be resolved via code changes. Manually addressing these errors is tedious and error-p…

Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Arduin Findeis, Floris Weers, Guoli Yin, Ke Ye, Ruoming Pang, Tom Gunter
https://arxiv.org/abs/2507.17015

Can External Validation Tools Improve Annotation Quality for LLM-as-a-Judge?
Pairwise preferences over model responses are widely collected to evaluate and provide feedback to large language models (LLMs). Given two alternative model responses to the same input, a human or AI annotator selects the "better" response. This approach can provide feedback for domains where other hard-coded metrics are difficult to obtain (e.g., chat response quality), thereby helping model evaluation or training. However, for some domains high-quality pairwise comparisons can be tricky to ob…

The iNaturalist Sounds Dataset
Mustafa Chasmai, Alexander Shepard, Subhransu Maji, Grant Van Horn
https://arxiv.org/abs/2506.00343 https://

The iNaturalist Sounds Dataset
We present the iNaturalist Sounds Dataset (iNatSounds), a collection of 230,000 audio files capturing sounds from over 5,500 species, contributed by more than 27,000 recordists worldwide. The dataset encompasses sounds from birds, mammals, insects, reptiles, and amphibians, with audio and species labels derived from observations submitted to iNaturalist, a global citizen science platform. Each recording in the dataset varies in length and includes a single species annotation. We benchmark multi…

Effective Multi-Task Learning for Biomedical Named Entity Recognition
Jo\~ao Ruano, Gon\c{c}alo M. Correia, Leonor Barreiros, Afonso Mendes
https://arxiv.org/abs/2507.18542 http…

Effective Multi-Task Learning for Biomedical Named Entity Recognition
Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types …

Partial Weakly-Supervised Oriented Object Detection
Mingxin Liu, Peiyuan Zhang, Yuan Liu, Wei Zhang, Yue Zhou, Ning Liao, Ziyang Gong, Junwei Luo, Zhirui Wang, Yi Yu, Xue Yang
https://arxiv.org/abs/2507.02751

Partial Weakly-Supervised Oriented Object Detection
The growing demand for oriented object detection (OOD) across various domains has driven significant research in this area. However, the high cost of dataset annotation remains a major concern. Current mainstream OOD algorithms can be mainly categorized into three types: (1) fully supervised methods using complete oriented bounding box (OBB) annotations, (2) semi-supervised methods using partial OBB annotations, and (3) weakly supervised methods using weak annotations such as horizontal boxes o…

Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink
Lisle Faray de Paiva, Gijs Luijten, Ana Sofia Ferreira Santos, Moon Kim, Behrus Puladi, Jens Kleesiek, Jan Egger
https://arxiv.org/abs/2506.04858

Beyond the Desktop: XR-Driven Segmentation with Meta Quest 3 and MX Ink
Medical imaging segmentation is essential in clinical settings for diagnosing diseases, planning surgeries, and other procedures. However, manual annotation is a cumbersome and effortful task. To mitigate these aspects, this study implements and evaluates the usability and clinical applicability of an extended reality (XR)-based segmentation tool for anatomical CT scans, using the Meta Quest 3 headset and Logitech MX Ink stylus. We develop an immersive interface enabling real-time interaction w…

Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Lianhao Yin, Ozanan Meireles, Guy Rosman, Daniela Rus
https://arxiv.org/abs/2506.01980

Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS). However, supervised learning approaches require large, annotated datasets that are scarce due to annotation efforts that are prohibitive, e.g., in medical fields. Although self-supervision methods can address such limitations, current self-supervised methods often fail to capture structural and physical information in a form that generalizes across tasks. We propose Compress-to-Explore (C2E), a no…

Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl, G\"oran Kauermann, Barbara Plank, Matthias A{\ss}enmacher
https://arxiv.org/abs/2507.02593

Revisiting Active Learning under (Human) Label Variation
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing, annotation frameworks often still rest on the assumption of a single ground truth. This overlooks human label variation (HLV), the occurrence of plausible differences in annotations, as an informative signal. Similarly, active learning (AL), a popular approach to optimizing th…

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark D\'iaz, Charvi Rastogi, Aida Davani, Vinodkumar Prabhakaran, Pushkar Mishra, Roma Patel, Alicia Parrish, Zoe Ashwood, Michela Paganini, Tian Huey Teh, Verena Rieser, Lora Aroyo
https://

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends …

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification
Kunlun Xu, Fan Zhuo, Jiangmeng Li, Xu Zou, Jiahuan Zhou
https://arxiv.org/abs/2507.01884

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification
Current lifelong person re-identification (LReID) methods predominantly rely on fully labeled data streams. However, in real-world scenarios where annotation resources are limited, a vast amount of unlabeled data coexists with scarce labeled samples, leading to the Semi-Supervised LReID (Semi-LReID) problem where LReID methods suffer severe performance degradation. Existing LReID methods, even when combined with semi-supervised strategies, suffer from limited long-term adaptation performance du…

Pixel Embedding Method for Tubular Neurite Segmentation
Huayu Fu, Jiamin Li, Haozhi Qu, Xiaolin Hu, Zengcai Guo
https://arxiv.org/abs/2507.23359 https://ar…

Pixel Embedding Method for Tubular Neurite Segmentation
Automatic segmentation of neuronal topology is critical for handling large scale neuroimaging data, as it can greatly accelerate neuron annotation and analysis. However, the intricate morphology of neuronal branches and the occlusions among fibers pose significant challenges for deep learning based segmentation. To address these issues, we propose an improved framework: First, we introduce a deep network that outputs pixel level embedding vectors and design a corresponding loss function, enabli…

Pull Requests From The Classroom: Co-Developing Curriculum And Code
Dennis Zyska, Ilia Kuznetsov, Florian M\"uller, Iryna Gurevych
https://arxiv.org/abs/2508.00646 https://…

Pull Requests From The Classroom: Co-Developing Curriculum And Code
Educational technologies often misalign with instructors' pedagogical goals, forcing adaptations that compromise teaching efficacy. In this paper, we present a case study on the co-development of curriculum and technology in the context of a university course on scientific writing. Specifically, we examine how a custom-built peer feedback system was iteratively developed alongside the course to support annotation, feedback exchange, and revision. Results show that while co-development fostered …

Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based
Shuangquan Lyu, Yingnan Deng, Guiran Liu, Zhen Qi, Ruotong Wang
https://arxiv.org/abs/2507.00601

Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based
This paper addresses the limited transfer and adaptation capabilities of large language models in low-resource language scenarios. It proposes a unified framework that combines a knowledge transfer module with parameter-efficient fine-tuning strategies. The method introduces knowledge alignment loss and soft prompt tuning to guide the model in effectively absorbing the structural features of target languages or tasks under minimal annotation. This enhances both generalization performance and tr…

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Jamil Fayyad, Nourhan Bayasi, Ziyang Yu, Homayoun Najjaran
https://arxiv.org/abs/2507.23001 https://

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Deep learning models for skin disease classification require large, diverse, and well-annotated datasets. However, such resources are often limited due to privacy concerns, high annotation costs, and insufficient demographic representation. While text-to-image diffusion probabilistic models (T2I-DPMs) offer promise for medical data synthesis, their use in dermatology remains underexplored, largely due to the scarcity of rich textual descriptions in existing skin image datasets. In this work, we…

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Fangyijie Wang, Kevin Whelan, F\'elix Balado, Gu\'enol\'e Silvestre, Kathleen M. Curran
https://arxiv.org/abs/2506.23664

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation by clinical experts. To overcome these challenges, synthetic medical data generation offers a promising solution. Generative AI (GenAI), employing generative deep learning models, has proven effective at producing realistic synthetic images. This study proposes a novel mask-guided GenAI approach using diffusion model…

Automated Label Placement on Maps via Large Language Models
Harry Shomer, Jiejun Xu
https://arxiv.org/abs/2507.22952 https://arxiv.org/pdf/2507.22952

Automated Label Placement on Maps via Large Language Models
Label placement is a critical aspect of map design, serving as a form of spatial annotation that directly impacts clarity and interpretability. Despite its importance, label placement remains largely manual and difficult to scale, as existing automated systems struggle to integrate cartographic conventions, adapt to context, or interpret labeling instructions. In this work, we introduce a new paradigm for automatic label placement (ALP) that formulates the task as a data editing problem and lev…

MDC-R: The Minecraft Dialogue Corpus with Reference
Chris Madge, Maris Camilleri, Paloma Carretero Garcia, Mladen Karan, Juexi Shao, Prashant Jayannavar, Julian Hough, Benjamin Roth, Massimo Poesio
https://arxiv.org/abs/2506.22062

MDC-R: The Minecraft Dialogue Corpus with Reference
We introduce the Minecraft Dialogue Corpus with Reference (MDC-R). MDC-R is a new language resource that supplements the original Minecraft Dialogue Corpus (MDC) with expert annotations of anaphoric and deictic reference. MDC's task-orientated, multi-turn, situated dialogue in a dynamic environment has motivated multiple annotation efforts, owing to the interesting linguistic phenomena that this setting gives rise to. We believe it can serve as a valuable resource when annotated with reference,…

Efficient Learning for Product Attributes with Compact Multimodal Models
Mandar Kulkarni
https://arxiv.org/abs/2507.19679 https://arxiv.org/pdf/2507.19679

Efficient Learning for Product Attributes with Compact Multimodal Models
Image-based product attribute prediction in e-commerce is a crucial task with numerous applications. The supervised fine-tuning of Vision Language Models (VLMs) faces significant scale challenges due to the cost of manual or API based annotation. In this paper, we investigate label-efficient semi-supervised fine-tuning strategies for compact VLMs (2B-3B parameters) that leverage unlabeled product listings through Direct Preference Optimization (DPO). Beginning with a small, API-based, annotated…

Towards Blind Bitstream-corrupted Video Recovery via a Visual Foundation Model-driven Framework
Tianyi Liu, Kejun Wu, Chen Cai, Yi Wang, Kim-Hui Yap, Lap-Pui Chau
https://arxiv.org/abs/2507.22481

Towards Blind Bitstream-corrupted Video Recovery via a Visual Foundation Model-driven Framework
Video signals are vulnerable in multimedia communication and storage systems, as even slight bitstream-domain corruption can lead to significant pixel-domain degradation. To recover faithful spatio-temporal content from corrupted inputs, bitstream-corrupted video recovery has recently emerged as a challenging and understudied task. However, existing methods require time-consuming and labor-intensive annotation of corrupted regions for each corrupted video frame, resulting in a large workload in…

Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
Rian Touchent, Nathan Godey, Eric de la Clergerie
https://arxiv.org/abs/2506.20331

Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content
We introduce Biomed-Enriched, a biomedical text dataset constructed from PubMed via a two-stage annotation process. In the first stage, a large language model annotates 400K paragraphs from PubMed scientific articles, assigning scores for their type (review, study, clinical case, other), domain (clinical, biomedical, other), and educational quality. The educational quality score (rated 1 to 5) estimates how useful a paragraph is for college-level learning. These annotations are then used to fin…

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Lei Zhu, Jun Zhou, Rick Siow Mong Goh, Yong Liu
https://arxiv.org/abs/2506.20563

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-supervised learning methods propose combinatorial CNN-Transformer learning to cross teach a transform…

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Si-Woo Kim, MinJu Jeon, Ye-Chan Kim, Soeun Lee, Taewhan Kim, Dong-Jin Kim
https://arxiv.org/abs/2507.18616

SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning
Zero-shot Image Captioning (ZIC) increasingly utilizes synthetic datasets generated by text-to-image (T2I) models to mitigate the need for costly manual annotation. However, these T2I models often produce images that exhibit semantic misalignments with their corresponding input captions (e.g., missing objects, incorrect attributes), resulting in noisy synthetic image-caption pairs that can hinder model training. Existing dataset pruning techniques are largely designed for removing noisy text in…