3 papers
cs.CV2024
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…
cs.CV2024
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…
cs.CV2024
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…