20 citations · 24 across the 4 of their papers we have counts for
5 papers
TimeRefine: Temporal Grounding with Time Refining Video LLM
Xizi Wang, Feng Cheng, Ziyang Wang +6
Video temporal grounding aims to localize relevant temporal boundaries in a video given a textual prompt. Recent work has focused on enabling Video LLMs to perform video temporal g…
VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos
Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin +4
Long-form video understanding is complicated by the high redundancy of video data and the abundance of query-irrelevant information. To tackle these challenges, we propose VideoTre…
DAM: Dynamic Adapter Merging for Continual Video QA Learning
Feng Cheng, Ziyang Wang, Yi-Lin Sung +3
We present a parameter-efficient method for continual video question-answering (VidQA) learning. Our method, named DAM, uses the proposed Dynamic Adapter Merging to (i) mitigate ca…
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Kristen Grauman, Andrew Westbury, Lorenzo Torresani +98
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric…
Unified Coarse-to-Fine Alignment for Video-Text Retrieval
Ziyang Wang, Yi-Lin Sung, Feng Cheng +2
The canonical approach to video-text retrieval leverages a coarse-grained or fine-grained alignment between visual and textual information. However, retrieving the correct video ac…