14 citations · 23 across the 23 of their papers we have counts for
7 papers · 2 filters
EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning
Yogesh Kulkarni, Pooyan Fazli
Egocentric video understanding requires procedural reasoning under partial observability and continuously shifting viewpoints. Current multimodal large language models (MLLMs) stru…
FrameOracle: Learning What to See and How Much to See in Videos
Chaoyu Li, Tianzhi Li, Fei Tao +6
Vision-language models (VLMs) advance video understanding but operate under tight computational budgets, making performance dependent on selecting a small, high-quality subset of f…
AVATAR: Reinforcement Learning to See, Hear, and Reason Over Video
Yogesh Kulkarni, Pooyan Fazli
Multimodal reasoning over long-horizon video is challenging due to the need for precise spatiotemporal fusion and alignment across modalities. While recent methods such as Group Re…
ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs
Chaoyu Li, Yogesh Kulkarni, Pooyan Fazli
The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing efficiency methods mainly target inference v…
VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment
Yogesh Kulkarni, Pooyan Fazli
Video-language models (Video-LLMs) excel at understanding video content but struggle with spatial relationships, temporal ordering, and cross-frame continuity. To address these lim…
ChartQA-X: Generating Explanations for Visual Chart Reasoning
Shamanthak Hegde, Pooyan Fazli, Hasti Seifi
The ability to explain complex information from chart images is vital for effective data-driven decision-making. In this work, we address the challenge of generating detailed expla…
VideoA11y: Method and Dataset for Accessible Video Description
Chaoyu Li, Sid Padmanabhuni, Maryam Cheema +2
Video descriptions are crucial for blind and low vision (BLV) users to access visual content. However, current artificial intelligence models for generating descriptions often fall…