Tootfinder

The ARIA annotations page on MDN (https://developer.mozilla.org/en-US/docs/Web/Accessibility/ARIA/Annotations) has been the tiniest of tiny thorns in my side for a while now.
I filed a PR to make it less… obfuscatory:

ARIA annotations - Accessibility | MDN
WAI-ARIA version 1.3 sees the addition of a set of new features, collectively known as ARIA annotations, which allow the creation of accessible annotations inside web documents. Typical use cases include edit suggestions (i.e. an addition and/or deletion in an editable document), and comments (e.g. an editorial comment related to a part of a document under review).

New Web App Feature – Reusing Annotations with Export / Import
https://web.hypothes.is/blog/reusing-annotations-with-export-import/
> We are excited to announce a significant enhancement to the

New Web App Feature – Reusing Annotations with Export / Import
https://web.hypothes.is/blog/reusing-annotations-with-export-import/
> We are excited to announce a significant enhancement to the

I don't know how to describe this well enough to search for it (successfully), but:
Are there general syntaxes for an AST/CST (AST=Abstract Syntax Tree, but I'm really interested in Concrete Syntax Trees that generally include enough information to round trip losslessly to and from source code).
An S-Expression is pretty good for an AST, but doesn't feel sufficient for a CST, where nodes have more extensive annotations.

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

But fuck off, saving annotations on PDF on Notes has been problematic for three years. I haven’t used it in a while until now so I just ass it was fixed by now (tho I have had and reported problems with saving on regular notes this year on iOS 17 beta. Tho those seems to be fixed, or at least I hope so)

I don't know how to describe this well enough to search for it (successfully), but:
Are there general syntaxes for an AST/CST (AST=Abstract Syntax Tree, but I'm really interested in Concrete Syntax Trees that generally include enough information to round trip losslessly to and from source code).
An S-Expression is pretty good for an AST, but doesn't feel sufficient for a CST, where nodes have more extensive annotations.

How We Define Harm Impacts Data Annotations: Explaining How Annotators Distinguish Hateful, Offensive, and Toxic Comments
Angela Schöpke-Gonzalez, Siqi Wu, Sagar Kumar, Paul J. Resnick, Libby Hemphill
https://arXiv.org/abs/2309.15827

Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Haoyu Liu, Fei Wang, Minmin Lin, Runze Wu, Renyu Zhu, Shiwei Zhao, Kai Wang, Tangjie Lv, Changjie Fan
https://arXiv.org/abs/2311.14709

Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crowd workers, modern techniques resort to redundant annotations and subsequent label aggregation to infer true labels. However, these methods require model updating during the inference, posing challenges in real-world implementation. Meanwhile, in recent years, many data labeling tasks have begun to require skilled and …

Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
Christoph Kern, Stephanie Eckman, Jacob Beck, Rob Chew, Bolei Ma, Frauke Kreuter
https://arXiv.org/abs/2311.14212

Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
When training data are collected from human annotators, the design of the annotation instrument, the instructions given to annotators, the characteristics of the annotators, and their interactions can impact training data. This study demonstrates that design choices made when creating an annotation instrument also impact the models trained on the resulting annotations. We introduce the term annotation sensitivity to refer to the impact of annotation data collection methods on the annotations …

This https://arxiv.org/abs/2309.09301 has been replaced.
Link: https://scholar.google.com/scholar?q=a

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. Th…

This https://arxiv.org/abs/2305.02967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Urgency Annotations for Alternating Choices
We propose urgency programs, a new programming model with support for alternation, imperfect information, and recursion. The novelty are urgency annotations that decorate the (angelic and demonic) choice operators and control the order in which alternation is resolved. We study standard notions of contextual equivalence for urgency programs. Our first main result are fully abstract characterizations of these relations based on sound and complete axiomatizations. Our second main result settles t…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

#ToolQuestion :BoostOK:
I have written a text (markdown) and now I want to send it (read-only) to some 10 people to gather feedback in form of text annotations.
I look for a web-based tool which allows to make highlights (text selections) and add comments to them (like LibreOffice Writer, Word, PDF Annotations, ...)
Individual commenters should not be required to sign in…

The Cambridge Law Corpus: A Corpus for Legal AI Research
https://arxiv.org/abs/2309.12269
HN: https://news.ycomb…

The Cambridge Law Corpus: A Corpus for Legal AI Research
We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts. Using our annotated data, we have trained and evaluated case outcome extracti…

This https://arxiv.org/abs/2309.09301 has been replaced.
Link: https://scholar.google.com/scholar?q=a

RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose Estimation
The current interacting hand (IH) datasets are relatively simplistic in terms of background and texture, with hand joints being annotated by a machine annotator, which may result in inaccuracies, and the diversity of pose distribution is limited. However, the variability of background, pose distribution, and texture can greatly influence the generalization ability. Therefore, we present a large-scale synthetic dataset RenderIH for interacting hands with accurate and diverse pose annotations. Th…

Weakly-Supervised Video Moment Retrieval via Regularized Two-Branch Proposal Networks with Erasing Mechanism
Haoyuan Li, Zhou Zhao, Zhu Zhang, Zhijie Lin
https://arXiv.org/abs/2311.13946

Weakly-Supervised Video Moment Retrieval via Regularized Two-Branch Proposal Networks with Erasing Mechanism
Video moment retrieval is to identify the target moment according to the given sentence in an untrimmed video. Due to temporal boundary annotations of the video are extremely time-consuming to acquire, modeling in the weakly-supervised setting is increasingly focused, where we only have access to the video-sentence pairs during training. Most existing weakly-supervised methods adopt a MIL-based framework to develop inter-sample confrontment, but neglect the intra-sample confrontment between mom…

Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Matthias Hadlich, Zdravko Marinov, Moon Kim, Enrico Nasca, Jens Kleesiek, Rainer Stiefelhagen
https://arXiv.org/abs/2311.14482

Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images
Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations. This requirement presents a challenge for whole-body Positron Emission Tomography (PET) imaging, where lesions are scattered throughout the body. To tackle this problem, we introduce SW-FastEdit - an interactive segmentation framework that accelerates the labeling by utilizing only a few user clicks instead of voxelw…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Didn't post bits of my own work for a while. So here's a demo of a #VFXPipeline tool I made to check continuity of sequences (color, amount of added haze...).
I used to do contact sheets in Nuke but inserting something once you have more than 5 thumbnails is a pain. I wanted to have a "thumbnail workspace" but not an image player.
This has hotkeys to arrange clips,…

Google launches Notes, an experiment to let users that opt-in via Search Labs annotate search results, including with images and colorful fonts (Jay Peters/The Verge)
https://www.theverge.com/2023/11/15/23961289/google-search-labs-notes…

Google is going to let you annotate search results
Google’s new “Notes” experiment will let users add annotations to search results. The company is also adding a way to follow specific queries.

Firefox will be adding the ability to stuff alt text into images and annotations within PDFs.
They have just shared a Figma of the #UX specs:
https://bugzilla.mozilla.org/show_bug.cgi?id=1844952#…

Interesting paper on using #LLM for #qualitative #research…
From: @…

Today I learned about RO-Crate 'a community effort to establish a lightweight approach to packaging research data with their metadata'. Using schema.org annotations in JSON-LD, they want to 'make best-practice in formal metadata description accessible and practical for use in a wider variety of situations' (via @… 's experiments with it)
'RO-Crate is…

This https://arxiv.org/abs/2305.02967 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Urgency Annotations for Alternating Choices
We propose urgency programs, a new programming model with support for alternation, imperfect information, and recursion. The novelty are urgency annotations that decorate the (angelic and demonic) choice operators and control the order in which alternation is resolved. We study standard notions of contextual equivalence for urgency programs. Our first main result are fully abstract characterizations of these relations based on sound and complete axiomatizations. Our second main result settles t…

#ToolQuestion :BoostOK:
I have written a text (markdown) and now I want to send it (read-only) to some 10 people to gather feedback in form of text annotations.
I look for a web-based tool which allows to make highlights (text selections) and add comments to them (like LibreOffice Writer, Word, PDF Annotations, ...)
Individual commenters should not be required to sign in…

Rust/Godot and Swift/Godot side by side, plotting richer type annotations for the bindings ( @… )

This https://arxiv.org/abs/2309.11038 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration
In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through com…

This https://arxiv.org/abs/2304.13032 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
Most machine learning and data analytics applications, including performance engineering in software systems, require a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. We develop a unified active learning framework specializing in software performance prediction to address this task. We begin by parsing the source code to an Abstract Syntax T…

This https://arxiv.org/abs/2311.00321 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning
With the proliferation of social media, accurate detection of hate speech has become critical to ensure safety online. To combat nuanced forms of hate speech, it is important to identify and thoroughly explain hate speech to help users understand its harmful effects. Recent benchmarks have attempted to tackle this issue by training generative models on free-text annotations of implications in hateful text. However, we find significant reasoning gaps in the existing annotations schemes, which ma…

Weakly-Supervised Video Moment Retrieval via Regularized Two-Branch Proposal Networks with Erasing Mechanism
Haoyuan Li, Zhou Zhao, Zhu Zhang, Zhijie Lin
https://arXiv.org/abs/2311.13946

Weakly-Supervised Video Moment Retrieval via Regularized Two-Branch Proposal Networks with Erasing Mechanism
Video moment retrieval is to identify the target moment according to the given sentence in an untrimmed video. Due to temporal boundary annotations of the video are extremely time-consuming to acquire, modeling in the weakly-supervised setting is increasingly focused, where we only have access to the video-sentence pairs during training. Most existing weakly-supervised methods adopt a MIL-based framework to develop inter-sample confrontment, but neglect the intra-sample confrontment between mom…

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images
Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan
https://arXiv.org/abs/2311.13993

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images
Information Extraction (IE) from document images is challenging due to the high variability of layout formats. Deep models such as LayoutLM and BROS have been proposed to address this problem and have shown promising results. However, they still require a large amount of field-level annotations for training these models. Other approaches using rule-based methods have also been proposed based on the understanding of the layout and semantics of a form such as geometric position, or type of the fi…

This https://arxiv.org/abs/2310.14897 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

The AIMI Initiative: AI-Generated Annotations for Imaging Data Commons Collections
The Image Data Commons (IDC) contains publicly available cancer radiology datasets that could be pertinent to the research and development of advanced imaging tools and algorithms. However, the full extent of its research capabilities is limited by the fact that these datasets have few if any, annotations associated with them. Through this study with the Artificial Intelligence (AI) AI in Medical Imaging (AIMI) initiative, we produced high-quality, AI-generated imaging annotations of tissues, o…

I've been enjoying using Reader by Readwise for work, and I'll probably start using it for my personal stuff too. Basically like Instapaper plus annotations from Kindle, plus AI summarizing all in one. And way more. It also integrates with nearly every service you can think of in terms of productivity.
I wish they had a native Mac app though, or let me install the iPad app on my Mac. If you'd interested in trying it for free for 30 days without a CC, please use my link!

Readwise
Grow wiser and retain books better: Readwise sends you a daily email resurfacing your best highlights from Kindle, Instapaper, iBooks, and more.

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images
Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan
https://arXiv.org/abs/2311.13993

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images
Information Extraction (IE) from document images is challenging due to the high variability of layout formats. Deep models such as LayoutLM and BROS have been proposed to address this problem and have shown promising results. However, they still require a large amount of field-level annotations for training these models. Other approaches using rule-based methods have also been proposed based on the understanding of the layout and semantics of a form such as geometric position, or type of the fi…

'GET_PANGENES: calling pangenes from plant genome alignments confirms presence-absence variation'
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-03071-z

GET_PANGENES: calling pangenes from plant genome alignments confirms presence-absence variation - Genome Biology
Crop pangenomes made from individual cultivar assemblies promise easy access to conserved genes, but genome content variability and inconsistent identifiers hamper their exploration. To address this, we define pangenes, which summarize a species coding potential and link back to original annotations. The protocol get_pangenes performs whole genome alignments (WGA) to call syntenic gene models based on coordinate overlaps. A benchmark with small and large plant genomes shows that pangenes recapi…

Anyone know of good Linux software for interfacing with microscope cameras (specifically ones that show up as standard UVC devices, not proprietary) and adding scale bars, annotations, etc to the images?
Or am I going to have to write something...

And it's done. The #OpenTelemetry demo now features:
* Explicit API in Java (on top of annotations and auto-instrumentation)
* A Node JS app for "analytics"
* A MQTT queue from Java to Node JS with context propagation 😎
I ponder to add a Ruby-on-Rails front-end 😊

Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models
Jake Chanenson, Madison Pickering, Noah Apthorpe
https://arXiv.org/abs/2311.02192

Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models
Identifying contextual integrity (CI) and governing knowledge commons (GKC) parameters in privacy policy texts can facilitate normative privacy analysis. However, GKC-CI annotation has heretofore required manual or crowdsourced effort. This paper demonstrates that high-accuracy GKC-CI parameter annotation of privacy policies can be performed automatically using large language models. We fine-tune 18 open-source and proprietary models on 21,588 GKC-CI annotations from 16 ground truth privacy pol…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Today we learn about #Rust's type annotations on function calls.

e.g. `collect::>()` which is nice and handy.

(Although I'd have thought the type system should be able to infer what it is by the fact I'm doing `map(|x| format!("blah blah", x)` which is essentially generating a `Vec<String>`.)

This https://arxiv.org/abs/2304.00466 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Learning Robust Medical Image Segmentation from Multi-source Annotations
Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of images. In this paper, we propose an Uncertainty-guided Multi-source Annotation Network (UMA-Net), which guides the training process by uncer…

This https://arxiv.org/abs/2310.14088 has been replaced.
Link: https://scholar.google.com/scholar?q=a

MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation
Curated datasets for healthcare are often limited due to the need of human annotations from experts. In this paper, we present MedEval, a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. MedEval is comprehensive and consists of data from several healthcare systems and spans 35 human body regions from 8 examination modalities. With 22,779 collected sentences and 21,228 reports, we provide expert annotations at multiple l…

Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations
Hanglei Zhang, Yiwei Guo, Sen Liu, Xie Chen, Kai Yu
https://arXiv.org/abs/2311.01260

Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations
Expressive text-to-speech (TTS) aims to synthesize speeches with human-like tones, moods, or even artistic attributes. Recent advancements in expressive TTS empower users with the ability to directly control synthesis style through natural language prompts. However, these methods often require excessive training with a significant amount of style-annotated data, which can be challenging to acquire. Moreover, they may have limited adaptability due to fixed style annotations. In this work, we pre…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

RedPajama v2 Open Dataset with 30T Tokens for Training LLMs
https://together.ai/blog/redpajama-data-v2
HN: https://

RedPajama-Data-v2: an Open Dataset with 30 Trillion Tokens for Training Large Language Models — Together AI
Releasing a new version of the RedPajama dataset, with 30 trillion filtered and deduplicated tokens (100+ trillions raw) from 84 CommonCrawl dumps covering 5 languages, along with 40+ pre-computed data quality annotations that can be used for further filtering and weighting.

This https://arxiv.org/abs/2309.05254 has been replaced.
Link: https://scholar.google.com/scholar?q=a

Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation
Depth estimation plays an important role in the robotic perception system. Self-supervised monocular paradigm has gained significant attention since it can free training from the reliance on depth annotations. Despite recent advancements, existing self-supervised methods still underutilize the available training data, limiting their generalization ability. In this paper, we take two data augmentation techniques, namely Resizing-Cropping and Splitting-Permuting, to fully exploit the potential of…

MMEAD: MS MARCO Entity Annotations and Disambiguations
Chris Kamphuis, Aileen Lin, Siwen Yang, Jimmy Lin, Arjen P. de Vries, Faegheh Hasibi
https://arXiv.org/abs/2309.07574 https://

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

The AIMI Initiative: AI-Generated Annotations for Imaging Data Commons Collections
Gowtham Krishnan Murugesan, Diana McCrumb, Mariam Aboian, Tej Verma, Rahul Soni, Fatima Memon, Jeff Van Oss
https://arXiv.org/abs/2310.14897

This https://arxiv.org/abs/2305.01698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_bot/

DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation
Deep learning and remote sensing techniques have significantly advanced water monitoring abilities; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DeepAqua, a self-supervised deep learning model that leverages knowledge distillation (a.k.a. teacher-student model) to eliminate the need for manual annotations d…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

This https://arxiv.org/abs/2308.06534 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_bot/

Dealing with Small Datasets for Deep Learning in Medical Imaging: An Evaluation of Self-Supervised Pre-Training on CT Scans Comparing Contrastive and Masked Autoencoder Methods for Convolutional Models
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be…

This https://arxiv.org/abs/2307.04052 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Learning to Group Auxiliary Datasets for Molecule
The limited availability of annotations in small molecule datasets presents a challenge to machine learning models. To address this, one common strategy is to collaborate with additional auxiliary datasets. However, having more data does not always guarantee improvements. Negative transfer can occur when the knowledge in the target dataset differs or contradicts that of the auxiliary molecule datasets. In light of this, identifying the auxiliary molecule datasets that can benefit the target dat…

This https://arxiv.org/abs/2310.10404 has been replaced.
Link: https://scholar.google.com/scholar?q=a

LLM4SGG: Large Language Model for Weakly Supervised Scene Graph Generation
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However, they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises…

This https://arxiv.org/abs/2307.14899 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Retrieval-based Text Selection for Addressing Class-Imbalanced Data in Classification
This paper addresses the problem of selecting of a set of texts for annotation in text classification using retrieval methods when there are limits on the number of annotations due to constraints on human resources. An additional challenge addressed is dealing with binary categories that have a small number of positive instances, reflecting severe class imbalance. In our situation, where annotation occurs over a long time period, the selection of texts to be annotated can be made in batches, wi…

scBeacon: single-cell biomarker extraction via identifying paired cell clusters across biological conditions with contrastive siamese networks
Chenyu Liu, Kweon Yong Jin, Jun Ding
https://arXiv.org/abs/2311.02594

scBeacon: single-cell biomarker extraction via identifying paired cell clusters across biological conditions with contrastive siamese networks
Despite the breakthroughs in biomarker discovery facilitated by differential gene analysis, challenges remain, particularly at the single-cell level. Traditional methodologies heavily rely on user-supplied cell annotations, focusing on individually expressed data, often neglecting the critical interactions between biological conditions, such as healthy versus diseased states. In response, here we introduce scBeacon, an innovative framework built upon a deep contrastive siamese network. scBeacon…

Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Mu Chen, Zhedong Zheng, Yi Yang
https://arXiv.org/abs/2311.12682 https://<…

Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces the need for manual pixel-level annotations in the target domain. To facilitate domain-invariant feature learning, existing methods typically mix data from both the source domain and target domain by simply copying and pasting the pixels. Such vanilla methods are usually sub-optimal since they do not take into account …

This https://arxiv.org/abs/2308.06477 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eessIV_bo

Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools consider exclusively the axial MRI direction in spite of the availability as per acquisition protocols of multi-view data. Further, when multi-view data is exploited, manual annotations and availability at test time for all the views is commonly assumed. In this work, we explore a contrastive approach at training time to lev…

This https://arxiv.org/abs/2309.11038 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration
In this paper, we present CaveSeg - the first visual learning pipeline for semantic segmentation and scene parsing for AUV navigation inside underwater caves. We address the problem of scarce annotated training data by preparing a comprehensive dataset for semantic segmentation of underwater cave scenes. It contains pixel annotations for important navigation markers (e.g. caveline, arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and open areas for servoing. Through com…

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Argumentation Element Annotation Modeling using XLNet
Christopher Ormerod, Amy Burkhardt, Mackenzie Young, Sue Lottridge
https://arXiv.org/abs/2311.06239 h…

Argumentation Element Annotation Modeling using XLNet
This study demonstrates the effectiveness of XLNet, a transformer-based language model, for annotating argumentative elements in persuasive essays. XLNet's architecture incorporates a recurrent mechanism that allows it to model long-term dependencies in lengthy texts. Fine-tuned XLNet models were applied to three datasets annotated with different schemes - a proprietary dataset using the Annotations for Revisions and Reflections on Writing (ARROW) scheme, the PERSUADE corpus, and the Argument A…

This https://arxiv.org/abs/2306.00639 has been replaced.
Link: https://scholar.google.com/scholar?q=a

Being Right for Whose Right Reasons?
Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are 'right for the right reasons'. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and…

This https://arxiv.org/abs/2310.10404 has been replaced.
Link: https://scholar.google.com/scholar?q=a

LLM4SGG: Large Language Model for Weakly Supervised Scene Graph Generation
Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However, they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises…

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation
Yinuo Wang, Kai Chen, Weimin Yuan, Cai Meng, XiangZhi Bai
https://arXiv.org/abs/2311.08190

SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation
Segment Anything Model (SAM), a vision foundation model trained on large-scale annotations, has recently continued raising awareness within medical image segmentation. Despite the impressive capabilities of SAM on natural scenes, it struggles with performance decline when confronted with medical images, especially those involving blurry boundaries and highly irregular regions of low contrast. In this paper, a SAM-based parameter-efficient fine-tuning method, called SAMIHS, is proposed for intra…

This https://arxiv.org/abs/2303.07898 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_bot/

ISLE: A Framework for Image Level Semantic Segmentation Ensemble
One key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels. Conventional semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. Hence, several works focus on semantic segmentation networks trained with only image-level annotations. However, when scrutinizing the results of state-of-the-art in more detail, we notice that they are remarkably close to each…

This https://arxiv.org/abs/2307.04657 has been replaced.
Link: https://scholar.google.com/scholar?q=a

BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
In this paper, we introduce the \textsc{BeaverTails} dataset, aimed at fostering research on safety alignment in large language models (LLMs). This dataset uniquely separates annotations of helpfulness and harmlessness for question-answering pairs, thus offering distinct perspectives on these crucial attributes. In total, we have gathered safety meta-labels for 30,207 question-answer (QA) pairs and 30,144 pairs of expert comparison data for both the helpfulness and harmlessness metrics. In tota…

This https://arxiv.org/abs/2310.17316 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich sem…

This https://arxiv.org/abs/2310.17316 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich sem…

This https://arxiv.org/abs/2303.14516 has been replaced.
Link: https://scholar.google.com/scholar?q=a

OVeNet: Offset Vector Network for Semantic Segmentation
Semantic segmentation is a fundamental task in visual scene understanding. We focus on the supervised setting, where ground-truth semantic annotations are available. Based on knowledge about the high regularity of real-world scenes, we propose a method for improving class predictions by learning to selectively exploit information from neighboring pixels. In particular, our method is based on the prior that for each pixel, there is a seed pixel in its close neighborhood sharing the same predicti…

This https://arxiv.org/abs/2301.09350 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

This https://arxiv.org/abs/2301.09350 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

This https://arxiv.org/abs/2308.16573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
Though supervised learning gains impressive success, the acquisition of indispensable large-scale labeled datasets are often impractical in biomedical imaging partially due to expensive costs and lengthy annotations done by experienced radiologists. Semi-supervised learning has been shown to be an effective way to address this limitation by leveraging useful information from unlabeled datasets. In this paper, we present a new semi-supervised learning method referred to as Dual-Decoder Consisten…

This https://arxiv.org/abs/2309.15702 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relationships. However, learning semantic 3D scene graphs in a fully supervised manner is inherently difficult as it requires not only object-level annotations but also relationship labels. While pre-training approaches have helped to boost the performance of many methods in various fields, pre-training for 3D scene graph pre…

This https://arxiv.org/abs/2309.15702 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relationships. However, learning semantic 3D scene graphs in a fully supervised manner is inherently difficult as it requires not only object-level annotations but also relationship labels. While pre-training approaches have helped to boost the performance of many methods in various fields, pre-training for 3D scene graph pre…

This https://arxiv.org/abs/2212.07207 has been replaced.
Link: https://scholar.google.com/scholar?q=arXiv…

MAELi -- Masked Autoencoder for Large-Scale LiDAR Point Clouds
We demonstrate how the often overlooked inherent properties of large-scale LiDAR point clouds can be effectively utilized for self-supervised representation learning. In pursuit of this goal, we design a highly data-efficient feature pre-training backbone that considerably reduces the need for tedious 3D annotations to train state-of-the-art object detectors. We propose Masked AutoEncoder for LiDAR point clouds (MAELi) that intuitively leverages the sparsity of LiDAR point clouds in both the en…