Hugging Face Trending Papers

From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification

Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-language methods aggregate a large pool of patch features into a single global slide representation.

arXiv AI

1d ago

DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis

arXiv:2608. 14719v1 Announce Type: cross Abstract: Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis.

By Xiaoxiao Li, Xitong Ling, Jiawen Li, Weiming Chen, Zhenyang Cai, Xidong Wang, Tian Guan, Benyou Wang, Yonghong He

benchmarks

arXiv AI

Aug 6

EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

arXiv:2608. 04472v1 Announce Type: cross Abstract: The development of foundation models (FMs) is crucial for advancing endoscopic image analysis.

By Zhenyu Yi, Jianwei Xu, Yue Hu, Zhongwei Qiu, Sijing Li, Liang Huang, Bin Lv, Ling Zhang, Yingda Xia

efficiency multimodal safety

Hugging Face Trending Papers

Jul 21

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image

Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence. However, most existing pathology benchmarks evaluate models on pre-cropped patches or pre-extracted slide features, leaving their ability to acquire evidence directly from gigapixel WSIs largely untested.

llms agents diffusion multimodal benchmarks

arXiv AI

Jul 22

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image

arXiv:2607. 19261v1 Announce Type: cross Abstract: Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrating multi-scale evidence.

By Dankai Liao, Tianyi Zhang, Yufeng Wu, Xinyue Zhang, Qiaochu Xue, Zeyu Liu, Dachun Zhao, Linghan Cai, Yueming Jin

llms agents diffusion multimodal benchmarks

arXiv AI

Jul 21

GMoT: Gated Motion-Aware Tokenization for Fine-Grained Micro-Gesture Video Reasoning with Multimodal LLMs

arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.

By Taorui Wang, Wei Xia, Hui Ma, Zijia Song, Jiayu Zhang, Zeheng Wang, Yong Xu, Zitong Yu

llms nlp multimodal

arXiv AI

Jul 3

Neuron-Aware Active Few-Shot Learning for LLMs

arXiv:2607. 02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance.

By Zhuowei Chen, Liwei Chen, Christian Schunn, Raquel Coelho, Xiang Lorraine Li

llms rag nlp benchmarks

arXiv AI

Jun 30

Data-Efficient Multimodal Alignment for Histopathology-based Molecular Prediction

arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.

By Dominik Winter, Dominik Vonficht, Lo\"ic Le Bescond, Christian Gebbe, Marco Rosati, Richard J. Chen, Markus Schick, Ross Stewart, Nicolas Brieu

llms fine-tuning multimodal safety

arXiv AI

Jun 24

CineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning

arXiv:2606. 24636v1 Announce Type: new Abstract: Cinematographic captioning aims to describe how a video is filmed using professional film-language concepts such as camera movement, shot size, depth of field, composition, and shooting angle.

By Xinyu Mao, Yuhui Zeng, Xiaokun Liu, Wenyu Qin, Meng Wang, Xin Tao, Pengfei Wan, Xiaohan Xing, Max Meng

llms diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 13

H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification

arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.

By Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang

rag benchmarks

arXiv AI

Jun 11

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

arXiv:2606. 11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult.

By Ruxue Shi, Yili Wang, Mengnan Du, Hangting Ye, Yi Chang, Xin Wang

llms benchmarks safety

arXiv Machine Learning

Jun 2

Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs

arXiv:2606. 01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification.

By Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie, Sarah Erfani

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 8

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.

By Yonghan Shin, Won-Ki Jeong

rag computer-vision efficiency benchmarks

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