arXiv:2211.15286
공식 코드 · Papers with Code
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
원문 초록
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made available.
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DisTime: Distribution-based Time Representation for Video Large Language Models
VideoMind: A Chain-of-LoRA Agent for Long Video Reasoning
TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation
Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding
Measure Twice, Cut Once: Grasping Video Structures and Event Semantics with LLMs for Video Temporal Localization
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