Tootfinder

DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing
Yasunori Toshimitsu, Kento Kawaharazuka, Akihiro Miki, Kei Okada, Masayuki Inaba
https://arxiv.org/abs/2507.00446

DIJE: Dense Image Jacobian Estimation for Robust Robotic Self-Recognition and Visual Servoing
For robots to move in the real world, they must first correctly understand the state of its own body and the tools that it holds. In this research, we propose DIJE, an algorithm to estimate the image Jacobian for every pixel. It is based on an optical flow calculation and a simplified Kalman Filter that can be efficiently run on the whole image in real time. It does not rely on markers nor knowledge of the robotic structure. We use the DIJE in a self-recognition process which can robustly disti…

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Yiming Lin, Yuchen Niu, Shang Wang, Kaizhu Huang, Qiufeng Wang, Xiao-Bo Jin
https://arxiv.org/abs/2508.21816

The Demon is in Ambiguity: Revisiting Situation Recognition with Single Positive Multi-Label Learning
Context recognition (SR) is a fundamental task in computer vision that aims to extract structured semantic summaries from images by identifying key events and their associated entities. Specifically, given an input image, the model must first classify the main visual events (verb classification), then identify the participating entities and their semantic roles (semantic role labeling), and finally localize these entities in the image (semantic role localization). Existing methods treat verb cl…

MetaLab: Few-Shot Game Changer for Image Recognition
Chaofei Qi, Zhitai Liu, Jianbin Qiu
https://arxiv.org/abs/2507.22057 https://arxiv.org/pdf/2507.22057

MetaLab: Few-Shot Game Changer for Image Recognition
Difficult few-shot image recognition has significant application prospects, yet remaining the substantial technical gaps with the conventional large-scale image recognition. In this paper, we have proposed an efficient original method for few-shot image recognition, called CIELab-Guided Coherent Meta-Learning (MetaLab). Structurally, our MetaLab comprises two collaborative neural networks: LabNet, which can perform domain transformation for the CIELab color space and extract rich grouped featur…

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Yui Tatsumi, Ziyue Zeng, Hiroshi Watanabe
https://arxiv.org/abs/2506.19297 https://…

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by image recognition models. This shift has drawn growing attention to scalable image compression methods that serve both machine and human vision (ICMH). Many existing models employ neural network-based codecs, known as learned image compression, and have made significant strides i…

Enhanced Image Recognition Using Gaussian Boson Sampling
Si-Qiu Gong, Ming-Cheng Chen, Hua-Liang Liu, Hao Su, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Yu-Hao Deng, Qian Wei, Hui Wang, Han-Sen Zhong, Xiao Jiang, Li Li, Nai-Le Liu, Chao-Yang Lu, Jian-Wei Pan
https://arxiv.org/abs/2506.19707

Enhanced Image Recognition Using Gaussian Boson Sampling
Gaussian boson sampling (GBS) has emerged as a promising quantum computing paradigm, demonstrating its potential in various applications. However, most existing works focus on theoretical aspects or simple tasks, with limited exploration of its capabilities in solving real-world practical problems. In this work, we propose a novel GBS-based image recognition scheme inspired by extreme learning machine (ELM) to enhance the performance of perceptron and implement it using our latest GBS device, J…

Entropy-Based Non-Invasive Reliability Monitoring of Convolutional Neural Networks
Amirhossein Nazeri, Wael Hafez
https://arxiv.org/abs/2508.21715 https://…

Entropy-Based Non-Invasive Reliability Monitoring of Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have become the foundation of modern computer vision, achieving unprecedented accuracy across diverse image recognition tasks. While these networks excel on in-distribution data, they remain vulnerable to adversarial perturbations imperceptible input modifications that cause misclassification with high confidence. However, existing detection methods either require expensive retraining, modify network architecture, or degrade performance on clean inputs. Here…

Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors
Patrick Iff, Paul Bruegger, Marcin Chrapek, Maciej Besta, Torsten Hoefler
https://arxiv.org/abs/2507.21989

Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors
Advances in embedding models for text, image, audio, and video drive progress across multiple domains, including retrieval-augmented generation, recommendation systems, vehicle/person reidentification, and face recognition. Many applications in these domains require an efficient method to retrieve items that are close to a given query in the embedding space while satisfying a filter condition based on the item's attributes, a problem known as Filtered Approximate Nearest Neighbor Search (FANNS)…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Contrastive Test-Time Composition of Multiple LoRA Models for Image Generation
Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- DSAGL: Dual-Stream Attention-Guided Learning for Weakly Supervised Whole Slide Image Classification
Cao, Cheng, Li, Zhou, Zhang, Li, Li, Gu, Zhang, Liu, Wu

Alibaba, Tencent, and other Chinese AI companies temporarily disabled chatbot functions like image recognition during China's annual college entrance exams (Luz Ding/Bloomberg)
https://www.bloomberg.com/news/articles/20

Normalized Radon Cumulative Distribution Transforms for Invariance and Robustness in Optimal Transport Based Image Classification
Matthias Beckmann, Robert Beinert, Jonas Bresch
https://arxiv.org/abs/2506.08761

Normalized Radon Cumulative Distribution Transforms for Invariance and Robustness in Optimal Transport Based Image Classification
The Radon cumulative distribution transform (R-CDT), is an easy-to-compute feature extractor that facilitates image classification tasks especially in the small data regime. It is closely related to the sliced Wasserstein distance and provably guaranties the linear separability of image classes that emerge from translations or scalings. In many real-world applications, like the recognition of watermarks in filigranology, however, the data is subject to general affine transformations originating…

Adjustable AprilTags For Identity Secured Tasks
Hao Li
https://arxiv.org/abs/2508.12304 https://arxiv.org/pdf/2508.12304

Adjustable AprilTags For Identity Secured Tasks
Special tags such as AprilTags that facilitate image processing and pattern recognition are useful in practical applications. In close and private environments, identity security is unlikely to be an issue because all involved AprilTags can be completely regulated. However, in open and public environments, identity security is no longer an issue that can be neglected. To handle potential harm caused by adversarial attacks, this note advocates utilization of adjustable AprilTags instead of fixed…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Image Captioning via Compact Bidirectional Architecture
Zijie Song, Yuanen Zhou, Zhenzhen Hu, Daqing Liu, Huixia Ben, Richang Hong, Meng Wang

Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025
Thien-Phuc Tran, Minh-Quang Nguyen, Minh-Triet Tran, Tam V. Nguyen, Trong-Le Do, Duy-Nam Ly, Viet-Tham Huynh, Khanh-Duy Le, Mai-Khiem Tran, Trung-Nghia Le
https://arxiv.org/abs/2508.18904

Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025
The Event-Enriched Image Analysis (EVENTA) Grand Challenge, hosted at ACM Multimedia 2025, introduces the first large-scale benchmark for event-level multimodal understanding. Traditional captioning and retrieval tasks largely focus on surface-level recognition of people, objects, and scenes, often overlooking the contextual and semantic dimensions that define real-world events. EVENTA addresses this gap by integrating contextual, temporal, and semantic information to capture the who, when, whe…

Neural networks with image recognition by pairs
Polad Geidarov
https://arxiv.org/abs/2506.06322 https://arxiv.org/pdf/2506.06322

Neural networks with image recognition by pairs
Neural networks based on metric recognition methods have a strictly determined architecture. Number of neurons, connections, as well as weights and thresholds values are calculated analytically, based on the initial conditions of tasks: number of recognizable classes, number of samples, metric expressions used. This paper discusses the possibility of transforming these networks in order to apply classical learning algorithms to them without using analytical expressions that calculate weight val…

Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition
Igor Kvasi\'c, Derek Orbaugh Antillon, {\DH}ula Na{\dj}, Christopher Walker, Iain Anderson, Nikola Mi\v{s}kovi\'c
https://arxiv.org/abs/2506.08974

Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition
In this paper, we present a dataset of diving gesture images used for human-robot interaction underwater. By offering this open access dataset, the paper aims at investigating the potential of using visual detection of diving gestures from an autonomous underwater vehicle (AUV) as a form of communication with a human diver. In addition to the image recording, the same dataset was recorded using a smart gesture recognition glove. The glove uses elastomer sensors and on-board processing to determ…

MF-LPR$^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
Kihyun Na, Junseok Oh, Youngkwan Cho, Bumjin Kim, Sungmin Cho, Jinyoung Choi, Injung Kim
https://arxiv.org/abs/2508.14797

MF-LPR$^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
License plate recognition (LPR) is important for traffic law enforcement, crime investigation, and surveillance. However, license plate areas in dash cam images often suffer from low resolution, motion blur, and glare, which make accurate recognition challenging. Existing generative models that rely on pretrained priors cannot reliably restore such poor-quality images, frequently introducing severe artifacts and distortions. To address this issue, we propose a novel multi-frame license plate re…

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
https://arxiv.org/abs/2506.00294

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key task…

IDFace: Face Template Protection for Efficient and Secure Identification
Sunpill Kim, Seunghun Paik, Chanwoo Hwang, Dongsoo Kim, Junbum Shin, Jae Hong Seo
https://arxiv.org/abs/2507.12050

IDFace: Face Template Protection for Efficient and Secure Identification
As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally c…

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Andrea Atzori, Fadi Boutros, Naser Damer
https://arxiv.org/abs/2508.13957 https://arxiv.or…

ViT-FIQA: Assessing Face Image Quality using Vision Transformers
Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA, a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token designed to predict a scalar utility score for any given face image. The le…

Identifying Prompted Artist Names from Generated Images
Grace Su, Sheng-Yu Wang, Aaron Hertzmann, Eli Shechtman, Jun-Yan Zhu, Richard Zhang
https://arxiv.org/abs/2507.18633 http…

Identifying Prompted Artist Names from Generated Images
A common and controversial use of text-to-image models is to generate pictures by explicitly naming artists, such as "in the style of Greg Rutkowski". We introduce a benchmark for prompted-artist recognition: predicting which artist names were invoked in the prompt from the image alone. The dataset contains 1.95M images covering 110 artists and spans four generalization settings: held-out artists, increasing prompt complexity, multiple-artist prompts, and different text-to-image models. We eval…

Real-Time Foreign Object Recognition Based on Improved Wavelet Scattering Deep Network and Edge Computing
He Zhichao, Shen Xiangyu, Zhang Yong, Xie Nan
https://arxiv.org/abs/2507.11043

Real-Time Foreign Object Recognition Based on Improved Wavelet Scattering Deep Network and Edge Computing
The increasing penetration rate of new energy in the power system has put forward higher requirements for the operation and maintenance of substations and transmission lines. Using the Unmanned Aerial Vehicles (UAV) to identify foreign object in real time can quickly and effectively eliminate potential safety hazards. However, due to the limited computation power, the captured image cannot be real-time processed on edge devices in UAV locally. To overcome this problem, a lightweight model based…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Efficient Image Generation with Variadic Attention Heads
Steven Walton, Ali Hassani, Xingqian Xu, Zhangyang Wang, Humphrey Shi

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky
https://arxiv.org/abs/2506.05588

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation
Chang, Fang, Xing, Wu, Cheng, Wang, Zeng, Yu, Chen

A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization
Cong Chen, Ming Chen, Hoileong Lee, Yan Li, Jiyang Yu
https://arxiv.org/abs/2507.15476

A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization
Surface defect detection of steel, especially the recognition of multi-scale defects, has always been a major challenge in industrial manufacturing. Steel surfaces not only have defects of various sizes and shapes, which limit the accuracy of traditional image processing and detection methods in complex environments. However, traditional defect detection methods face issues of insufficient accuracy and high miss-detection rates when dealing with small target defects. To address this issue, this…

Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Ling Li, Yao Zhou, Yuxuan Liang, Fugee Tsung, Jiaheng Wei
https://arxiv.org/abs/2506.14674

Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability. The rise of large vision-language models (LVLMs) has enabled a rethinking of geo-localization as a reasoning-driven task grounded in visual cues. However, two major challenges persist. On the data side, existing reasoning-focused datasets are primarily based on street-view imagery, offering limited scene diversity and…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Image Super-Resolution with Guarantees via Conformalized Generative Models
Eduardo Adame, Daniel Csillag, Guilherme Tegoni Goedert

SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Alessio Caviglia, Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
https://arxiv.org/abs/2507.10561

SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particularly promising due to their event-driven and temporally sparse nature, making them well-suited for low-power Field Programmable Gate Array (FPGA)-based deployment. This paper explores using the open-source Spiker+ framework to generate optimized SNNs accelerators for handwritten digit recognition on the MNIST dataset. S…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching
Yepeng Liu, Zhichao Sun, Baosheng Yu, Yitian Zhao, Bo Du, Yongchao Xu, Jun Cheng

GM-Skip: Metric-Guided Transformer Block Skipping for Efficient Vision-Language Models
Lianming Huang, Haibo Hu, Qiao Li, Xin He, Nan Guan, Chun Jason Xue
https://arxiv.org/abs/2508.18227

GM-Skip: Metric-Guided Transformer Block Skipping for Efficient Vision-Language Models
Transformer-based Vision-Language Models (VLMs) have achieved impressive performance on tasks such as image captioning, object recognition, and visual reasoning, but their high computational cost hinders deployment in latency-sensitive applications like autonomous driving. We introduce GM-Skip, a flexible and metric-adaptive framework for Transformer block skipping that accelerates VLM inference while preserving output quality. GM-Skip features a greedy, metric-guided block selection strategy t…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Time-Aware One Step Diffusion Network for Real-World Image Super-Resolution
Tainyi Zhang, Zheng-Peng Duan, Peng-Tao Jiang, Bo Li, Ming-Ming Cheng, Chun-Le Guo, Chongyi Li

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/6]:
- GenLit: Reformulating Single-Image Relighting as Video Generation
Shrisha Bharadwaj, Haiwen Feng, Giorgio Becherini, Victoria Fernandez Abrevaya, Michael J. Black

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Erez Meoded
https://arxiv.org/abs/2508.11499 https://arxiv.org/pdf/25…

Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Historical handwritten text recognition (HTR) is essential for unlocking the cultural and scholarly value of archival documents, yet digitization is often hindered by scarce transcriptions, linguistic variation, and highly diverse handwriting styles. In this study, we apply TrOCR, a state-of-the-art transformer-based HTR model, to 16th-century Latin manuscripts authored by Rudolf Gwalther. We investigate targeted image preprocessing and a broad suite of data augmentation techniques, introducing…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation
Md Shazid Islam, Sayak Nag, Arindam Dutta, Miraj Ahmed, Fahim Faisal Niloy, Amit K. Roy-Chowdhury

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/6]:
- TARDIS STRIDE: A Spatio-Temporal Road Image Dataset and World Model for Autonomy
Carri\'on, Bai, Castro, Panaganti, Zenith, Trang, Zhang, Perona, Malik

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer
Tao Tang, Chengxu Yang
https://arxiv.org/abs/2508.07817 https://

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer
The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/6]:
- Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning
Runmin Jiang, Zhaoxin Fan, Junhao Wu, Lenghan Zhu, Xin Huang, Tianyang Wang, Heng Huang, Min Xu

This https://arxiv.org/abs/2506.05588 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Preprocessing Methods for Memristive Reservoir Computing for Image Recognition
Reservoir computing (RC) has attracted attention as an efficient recurrent neural network architecture due to its simplified training, requiring only its last perceptron readout layer to be trained. When implemented with memristors, RC systems benefit from their dynamic properties, which make them ideal for reservoir construction. However, achieving high performance in memristor-based RC remains challenging, as it critically depends on the input preprocessing method and reservoir size. Despite …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image
Hyeongjin Nam, Donghwan Kim, Gyeongsik Moon, Kyoung Mu Lee

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning
Bob Zhang, Haoran Li, Tao Zhang, Cilin Yan, Jiayin Cai, Yanbin Hao

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Context Diffusion: In-Context Aware Image Generation
Najdenkoska, Sinha, Dubey, Mahajan, Ramanathan, Radenovic

Attributes Shape the Embedding Space of Face Recognition Models
Pierrick Leroy, Antonio Mastropietro, Marco Nurisso, Francesco Vaccarino
https://arxiv.org/abs/2507.11372

Attributes Shape the Embedding Space of Face Recognition Models
Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/7]:
- Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distrib...
Behraj Khan, Tahir Qasim Syed, Nouman M. Durrani, Bilal Naseem, Shabir Ahmad, Rizwan Qureshi
…

Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Zofia Rudnicka, Januszcz Szczepanski, Agnieszka Pregowska
https://arxiv.org/abs/2506.06265

Integrating Complexity and Biological Realism: High-Performance Spiking Neural Networks for Breast Cancer Detection
Spiking Neural Networks (SNNs) event-driven nature enables efficient encoding of spatial and temporal features, making them suitable for dynamic time-dependent data processing. Despite their biological relevance, SNNs have seen limited application in medical image recognition due to difficulties in matching the performance of conventional deep learning models. To address this, we propose a novel breast cancer classification approach that combines SNNs with Lempel-Ziv Complexity (LZC) a computat…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/3]:
- Color Image Set Recognition Based on Quaternionic Grassmannians
Xiang Xiang Wang, Tin-Yau Tam

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Omni$^2$: Unifying Omnidirectional Image Generation and Editing in an Omni Model
Yang, Duan, Zhu, Liu, Liu, Xu, Ma, Min, Zhai, Le Callet

A Lightweight Face Quality Assessment Framework to Improve Face Verification Performance in Real-Time Screening Applications
Ahmed Aman Ibrahim, Hamad Mansour Alawar, Abdulnasser Abbas Zehi, Ahmed Mohammad Alkendi, Bilal Shafi Ashfaq Ahmed Mirza, Shan Ullah, Ismail Lujain Jaleel, Hassan Ugail
https://arxiv.org/abs/2507.15961

A Lightweight Face Quality Assessment Framework to Improve Face Verification Performance in Real-Time Screening Applications
Face image quality plays a critical role in determining the accuracy and reliability of face verification systems, particularly in real-time screening applications such as surveillance, identity verification, and access control. Low-quality face images, often caused by factors such as motion blur, poor lighting conditions, occlusions, and extreme pose variations, significantly degrade the performance of face recognition models, leading to higher false rejection and false acceptance rates. In th…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- From Image Captioning to Visual Storytelling
Admitos Passadakis, Yingjin Song, Albert Gatt

This https://arxiv.org/abs/2505.24380 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

SASP: Strip-Aware Spatial Perception for Fine-Grained Bird Image Classification
Fine-grained bird image classification (FBIC) is not only of great significance for ecological monitoring and species identification, but also holds broad research value in the fields of image recognition and fine-grained visual modeling. Compared with general image classification tasks, FBIC poses more formidable challenges: 1) the differences in species size and imaging distance result in the varying sizes of birds presented in the images; 2) complex natural habitats often introduce strong ba…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/7]:
- Hyperspectral Image Generation with Unmixing Guided Diffusion Model
Shiyu Shen, Bin Pan, Ziye Zhang, Zhenwei Shi

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/7]:
- ForensicsSAM: Toward Robust and Unified Image Forgery Detection and Localization Resisting to Adv...
Rongxuan Peng, Shunquan Tan, Chenqi Kong, Anwei Luo, Alex C. Kot, Jiwu Huang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/7]:
- Adaptively Clustering Neighbor Elements for Image-Text Generation
Zihua Wang, Xu Yang, Hanwang Zhang, Haiyang Xu, Ming Yan, Fei Huang, Yu Zhang

DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu, Keze Wang, Liang Lin
https://arxiv.org/abs/2508.05585 https:/…

DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition
Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image, requiring both precise intra-class localization to pinpoint objects and effective inter-class reasoning to model complex category dependencies. While Vision-Language Pre-training (VLP) models offer a strong open-vocabulary foundation, they often struggle with fine-grained localization under weak supervision and typically fail to explicitly leverage structured relational …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
FrameBridge: Improving Image-to-Video Generation with Bridge Models
http…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[8/9]:
- FQGA-single: Towards Fewer Training Epochs and Fewer Model Parameters for Image-to-Image Translat...
Cho Yang

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/3]:
FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast

Large Language Models Facilitate Vision Reflection in Image Classification
Guoyuan An, JaeYoon Kim, SungEui Yoon
https://arxiv.org/abs/2508.06525 https://a…

Large Language Models Facilitate Vision Reflection in Image Classification
This paper presents several novel findings on the explainability of vision reflection in large multimodal models (LMMs). First, we show that prompting an LMM to verify the prediction of a specialized vision model can improve recognition accuracy, even on benchmarks like ImageNet, despite prior evidence that LMMs typically underperform dedicated vision encoders. Second, we analyze the internal behavior of vision reflection and find that the vision-language connector maps visual features into exp…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
Yusu Qian, Jiasen Lu, Tsu-Jui Fu, Xinze Wang, Chen Chen, Yinfei Yang, Wenze Hu, Zhe Gan

Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
Tong Xiang, Hongxia Zhao, Fenghua Zhu, Yuanyuan Chen, Yisheng Lv
https://arxiv.org/abs/2508.13823

Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
Achieving top-notch performance in Intelligent Transportation detection is a critical research area. However, many challenges still need to be addressed when it comes to detecting in a cross-domain scenario. In this paper, we propose a Self-Aware Adaptive Alignment (SA3), by leveraging an efficient alignment mechanism and recognition strategy. Our proposed method employs a specified attention-based alignment module trained on source and target domain datasets to guide the image-level features a…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/9]:
- Prompt-Softbox-Prompt: A Free-Text Embedding Control for Image Editing
Yitong Yang, Yinglin Wang, Tian Zhang, Jing Wang, Shuting He

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
Hongfei Zhang, Kun Zhou, Ruizheng Wu, Jiangbo Lu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/10]:
- MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Mokshagna Sai Teja Karanam, Krithika Iyer, Sarang Joshi, Shireen Elhabian

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[8/10]:
- AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal La...
Zhou, Luo, Wu, Sun, Ji, Yan, Ding, Sun, Wu, Ji

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/6]:
- CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation
Hui Zhang, Dexiang Hong, Yitong Wang, Jie Shao, Xinglong Wu, Zuxuan Wu, Yu-Gang Jiang

A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Oktay Karakus
https://arxiv.org/abs/2508.06407

A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/10]:
- Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation
Nadav Z. Cohen, Oron Nir, Ariel Shamir

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/6]:
- BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Mengen Cai, Zijian Zhang, Yubing Lu, Qiuzhan Zhou

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Training-free Geometric Image Editing on Diffusion Models
Hanshen Zhu, Zhen Zhu, Kaile Zhang, Yiming Gong, Yuliang Liu, Xiang Bai

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- FaceLift: Learning Generalizable Single Image 3D Face Reconstruction from Synthetic Heads
Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Pegios, Lin, Weng, Svendsen, Bashir, Bigdeli, Christensen, Tolsgaard, Feragen