1.5k citations · 1.7k across the 59 of their papers we have counts for
8 papers · 2 filters
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…
ReVisionLLM: Recursive Vision-Language Model for Temporal Grounding in Hour-Long Videos
Tanveer Hannan, Md Mohaiminul Islam, Jindong Gu +2
Large language models (LLMs) excel at retrieving information from lengthy text, but their vision-language counterparts (VLMs) face difficulties with hour-long videos, especially fo…
Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videos
Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang +4
Goal-oriented planning, or anticipating a series of actions that transition an agent from its current state to a predefined objective, is crucial for developing intelligent assista…
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…
Siamese Vision Transformers are Scalable Audio-visual Learners
Yan-Bo Lin, Gedas Bertasius
Traditional audio-visual methods rely on independent audio and visual backbones, which is costly and not scalable. In this work, we investigate using an audio-visual siamese networ…