Ego-VPA: Egocentric Video Understanding with Parameter-efficient Adaptation

• URL: http://arxiv.org/abs/2407.19520v1
• Date: Sun, 28 Jul 2024 16:01:32 GMT
• Title: Ego-VPA: Egocentric Video Understanding with Parameter-efficient Adaptation
• Authors: Tz-Ying Wu, Kyle Min, Subarna Tripathi, Nuno Vasconcelos,
• Abstract summary: Ego-VPA is a parameter-efficient adaptation for egocentric video tasks. Ego-VPA excels in lightweight adaptation with only 0.84% learnable parameters.
• Score: 57.38965505987893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video understanding typically requires fine-tuning the large backbone when adapting to new domains. In this paper, we leverage the egocentric video foundation models (Ego-VFMs) based on video-language pre-training and propose a parameter-efficient adaptation for egocentric video tasks, namely Ego-VPA. It employs a local sparse approximation for each video frame/text feature using the basis prompts, and the selected basis prompts are used to synthesize video/text prompts. Since the basis prompts are shared across frames and modalities, it models context fusion and cross-modal transfer in an efficient fashion. Experiments show that Ego-VPA excels in lightweight adaptation (with only 0.84% learnable parameters), largely improving over baselines and reaching the performance of full fine-tuning.

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