Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition

Congqi Cao, Yueran Zhang, Yating Yu, Qinyi Lv, Lingtong Min, Yanning Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Existing works in few-shot action recognition mostly fine-tune a pre-trained image model and design sophisticated temporal alignment modules at feature level. However, simply fully fine-tuning the pre-trained model could cause overfitting due to the scarcity of video samples. Additionally, we argue that the exploration of task-specific information is insufficient when relying solely on well extracted abstract features. In this work, we propose a simple but effective task-specific adaptation method (Task-Adapter) for few-shot action recognition. By introducing the proposed Task-Adapter into the last several layers of the backbone and keeping the parameters of the original pre-trained model frozen, we mitigate the overfitting problem caused by full fine-tuning and advance the task-specific mechanism into the process of feature extraction. In each Task-Adapter, we reuse the frozen self-attention layer to perform task-specific self-attention across different videos within the given task to capture both distinctive information among classes and shared information within classes, which facilitates task-specific adaptation and enhances subsequent metric measurement between the query feature and support prototypes. Experimental results consistently demonstrate the effectiveness of our proposed Task-Adapter on four standard few-shot action recognition datasets. Especially on temporal challenging SSv2 dataset, our method outperforms the state-of-the-art methods by a large margin.

Original languageEnglish
Title of host publicationMM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages9038-9047
Number of pages10
ISBN (Electronic)9798400706868
DOIs
StatePublished - 28 Oct 2024
Event32nd ACM International Conference on Multimedia, MM 2024 - Melbourne, Australia
Duration: 28 Oct 20241 Nov 2024

Publication series

NameMM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia

Conference

Conference32nd ACM International Conference on Multimedia, MM 2024
Country/TerritoryAustralia
CityMelbourne
Period28/10/241/11/24

Keywords

  • few-shot action recognition
  • parameter-efficient fine-tuning
  • task-specific adaptation

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