arXiv Machine Learning

A Realistic Protocol for Evaluation of Weakly Supervised Object Localization

arXiv:2404. 10034v3 Announce Type: replace-cross Abstract: Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels.

arXiv AI

Jun 2

MoEIoU: Rethinking Bounding-Box Regression as a Mixture of Experts

arXiv:2606. 00844v1 Announce Type: cross Abstract: Bounding-box regression is a fundamental component of object detection, playing a critical role in precise object localization.

By Vinay Edula, Priyanka Bagade

computer-vision benchmarks safety

arXiv Machine Learning

Jul 14

Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images

arXiv:2607. 10783v1 Announce Type: cross Abstract: Whole-slide images (WSIs) provide rich tissue-level and cellular-level information, but storing and transmitting high-magnification pathology data is resource-intensive.

By Dung Minh Do, Nhat-Thanh Huynh, Duc Minh Huynh, Doanh C. Bui, Khang Nguyen

computer-vision benchmarks

arXiv AI

Jul 21

Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs

arXiv:2607. 18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts.

By Yi Tang, Xinyi Shang, Jiacheng Cui, Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tran Dinh Tien, Ahmed Elhagry, Salwa K. Al Khatib, Tianjun Yao, Yonina C. Eldar, Jing-Hao Xue, Hao Li, Salman Khan, Zhiqiang Shen

llms diffusion robotics multimodal benchmarks

arXiv AI

Jun 11

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation

arXiv:2510. 06596v2 Announce Type: replace-cross Abstract: The performance of machine learning models depends heavily on training data.

By Ayush Zenith, Arnold Zumbrun, Neel Raut, Jing Lin

computer-vision data-engineering

arXiv AI

Jul 3

ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection

arXiv:2607. 01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training.

By Jae-Ryung Hong, Ho-Joong Kim, Seong-Whan Lee

rag computer-vision multimodal

arXiv AI

Jun 2

Aligning Cellular Sheaves with Classifier Attention for Interpretable Weakly-Supervised Pathology Localization

arXiv:2606. 00092v1 Announce Type: cross Abstract: Weakly-supervised classification of whole-slide images with attention-based multiple instance learning (ABMIL) on top of foundation features now reaches near-saturation on Camelyon16 slide-level performance, but the corresponding attention maps are an imperfect localization signal: in clinical interpretation, a model that classifies correctly without firing on the actual lesion is hard to trust.

By Devansh Lalwani, Swapnil Bhat, Maulik Shah

benchmarks

arXiv Machine Learning

Jun 2

Self-Improving Small Object Grounding in LVLMs

arXiv:2606. 01612v1 Announce Type: cross Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning?

By Tianze Yang, Yucheng Shi, Ruitong Sun, Ninghao Liu, Jin Sun

llms fine-tuning safety

arXiv AI

Jul 21

Noise-Robust Box-Supervised Infrared Small Target Detection via Physics-Inspired Soft Label Optimization

arXiv:2607. 17148v1 Announce Type: cross Abstract: Infrared small target detection (IRSTD) commonly relies on pixel-level mask supervision.

By Xizhe Zhang, Fan Shi, Mianzhao Wang, Jiangpeng Zheng, Xu Cheng, Shengyong Chen

computer-vision

arXiv Machine Learning

Jul 10

A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding

arXiv:2512. 21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools.

By Christina Liu, Alan Q. Wang, Joy Hsu, Jiajun Wu, Ehsan Adeli

llms multimodal benchmarks

arXiv AI

Jun 2

CalArena: A Large-Scale Post-Hoc Calibration Benchmark

arXiv:2605. 30188v2 Announce Type: replace-cross Abstract: Reliable probability estimates are critical in many machine learning applications, yet modern classifiers are often poorly calibrated.

By Eug\`ene Berta, David Holzm\"uller, Francis Bach, Michael I. Jordan

computer-vision benchmarks

arXiv Machine Learning

Jul 14

BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammography

arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.

By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat

llms computer-vision benchmarks

arXiv AI

Jun 16

Tool-IQA: Augmenting Image Quality Assessment with Simple Tools

arXiv:2606. 16082v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA).

By Guanyi Qin, Junjie Zhang, Chunming He, Yibing Fu, Jie Liang, Tianhe Wu, Lei Zhang

llms agents multimodal benchmarks

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