7 citations · 20 across the 16 of their papers we have counts for
7 papers · 1 filter
HeiCo-FOCUS: A Clinically Grounded Dataset for Long-Context Video Understanding
Leon Mayer, Lucas Luttner, Patrick Godau +42
Recent advances in Vision-Language Models (VLMs) have led to rapid progress in video understanding across a wide range of benchmark tasks. However, existing evaluations largely foc…
Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding
Hanoona Rasheed, Haania Siddiqui, Ming-Hsuan Yang +2
Spatio-temporal video grounding (STVG) requires models to identify when a referred event occurs and localize the target entity throughout that interval. Existing multimodal large l…
Training-Free Speech-Centric Omni Understanding with Frozen VLMs
Ankan Deria, Hanoona Rasheed, Xilin He +2
Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships. Existing omni models typically…
CORTEX: A Structured Reasoning Benchmark for Trustworthy 3D Chest CT MLLMs
Hashmat Shadab Malik, Anees Ur Rehman Hashmi, Numan Saeed +3
Reasoning in multimodal large language models (MLLMs) has shown strong promise in medical imaging. However, this reasoning is usually free-form text judged only by its final answer…
Ask, Solve, Generate: Self-Evolving Unified Multimodal Understanding and Generation via Self-Consistency Rewards
Ritesh Thawkar, Shravan Venkatraman, Omkar Thawakar +5
Most unified large multimodal models (LMMs) that support both visual understanding and image generation still rely on curated post-training supervision, such as human annotations,…
Paying More Attention to Visual Tokens in Self-Evolving Large Multimodal Models
Shravan Venkatraman, Ritesh Thawkar, Omkar Thawakar +4
Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However, multi-role self-play and se…
DocAtlas: Multilingual Document Understanding Across 80+ Languages
Ahmed Heakl, Youssef Mohamed, Abdullah Sohail +6
Multilingual document understanding remains limited for low-resource languages due to scarce training data and model-based annotation pipelines that perpetuate existing biases. We…