EVA: Enabling Video Attributes With Hierarchical Prompt Tuning for Action Recognition

The pretraining and fine-tuning paradigm has excelled in action recognition. However, full fine-tuning is computationally and storage costly, while parameter-efficient fine-tuning (PEFT) always sacrifices accuracy and stability. To address these challenges, we propose a novel method, Enabling Video Attributes with Hierarchical Prompt Tuning (EVA), to guide action recognition. Firstly, instead of focusing solely on temporal features, EVA sparsely extracts six types of video attributes across two modalities, capturing the relatively gradual attribute changes in actions. Secondly, a hierarchical prompt tuning architecture with multiscale attribute prompts is introduced to learn the differences in actions. Finally, by adjusting only a small number of additional parameters, EVA outperforms all PEFT and most full fine-tuning methods across four widely used datasets (Something-Something V2, ActivityNet, HMDB51, and UCF101), demonstrating its effectiveness.