arXiv Computer Vision

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

arXiv Machine Learning

Jul 9

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.

By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia

llms nlp fine-tuning multimodal

arXiv Machine Learning

Jul 7

Incentivizing Vision Language Models to Search for Long Video Question Answering

arXiv:2607. 02959v1 Announce Type: cross Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process.

By Harsh Goel, S P Sharan, Sahil Shah, Minkyu Choi, Joungbin An, Kristen Grauman, Sandeep P. Chinchali

llms agents nlp reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 8

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown remarkable performance in text-vision tasks, their robustness and transferability to different document domains remains underexplored.

llms nlp fine-tuning multimodal

arXiv AI

Aug 7

Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

arXiv:2604. 01280v2 Announce Type: replace-cross Abstract: Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence.

By Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

llms nlp multimodal benchmarks

arXiv Machine Learning

Jun 15

Self-Evolving Visual Questioner

arXiv:2606. 13929v1 Announce Type: cross Abstract: Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains underexplored.

By Yijun Liang, Hengguang Zhou, Ming Li, Lichen Li, Cho-Jui Hsieh, Tianyi Zhou

llms agents multimodal

arXiv Computation and Language

Sep 1

ReVA: A Region-Aware Visual Assistant for Visually Grounded Question Answering

arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...

By Anoop Senthil

llms rag computer-vision nlp multimodal safety

arXiv Computation and Language

4d ago

Question-Specific Knowledge Graphs for Efficient Visual Reasoning

arXiv:2609.35942v1 Announce Type: new Abstract: Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs i...

By Ting-Chih Chen, Emile van Krieken, Shujian Yu, Filip Ilievski

llms nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jun 16

When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs

arXiv:2602. 00344v2 Announce Type: replace-cross Abstract: While Retrieval-Augmented Generation (RAG) is one of the dominant paradigms for enhancing Large Vision-Language Models (LVLMs) on knowledge-based VQA tasks, recent work attributes RAG failures to insufficient attention towards the retrieved context, proposing to reduce the attention allocated to image tokens.

By Beidi Zhao, Wenlong Deng, Xinting Liao, Yushu Li, Nazim Shaikh, Yao Nie, Xiaoxiao Li

llms rag multimodal

arXiv AI

Aug 25

Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding

arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...

By Changjiang Jiang, Qiannian Zhao, Lei Xin, Jinxiang Xie, Preslav Nakov, Zhuohan Xie

llms reinforcement-learning fine-tuning efficiency multimodal

arXiv AI

Aug 14

Polish Medical Visual Question Answering: Vision-Language Models Underutilize Visual Evidence

arXiv:2608. 12928v1 Announce Type: new Abstract: We introduce a Polish-language medical visual question answering (VQA) benchmark, built from Polish Board Certification Examination questions for licensed physicians and dentists pursuing specialist certification.

By Jakub Pokrywka, {\L}ukasz Grzybowski, Antoni Lasik, Marek Kubis, Jeremi Ignacy Kaczmarek, Wojciech Kusa

llms nlp multimodal benchmarks

arXiv Computer Vision

Sep 15

HANCLIP: A Family of Hyperbolic Angular Negation Vision Language Models

arXiv:2606.23843v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve strong cross-modal alignment but remain brittle to negation, often relying on shallow word associations rathe...

By Hoang-Bao Le, Aiden Durrant, Thai Son Mai, Binh T. Nguyen, Liting Zhou, Cathal Gurrin

llms rag fine-tuning multimodal benchmarks safety

arXiv AI

Jun 3

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

arXiv:2606. 03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning.

By Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch

llms nlp efficiency multimodal safety

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