D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition

• URL: http://arxiv.org/abs/2312.01431v3
• Date: Sat, 20 Apr 2024 14:15:36 GMT
• Title: D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
• Authors: Wenjie Pei, Qizhong Tan, Guangming Lu, Jiandong Tian,
• Abstract summary: Adapting large pre-trained image models to few-shot action recognition has proven to be an effective strategy for learning robust feature extractors. We present the Disentangled-and-Deformable Spatio-Temporal Adapter (D$2$ST-Adapter), which is a novel tuning framework well-suited for few-shot action recognition.
• Score: 60.84084172829169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adapting large pre-trained image models to few-shot action recognition has proven to be an effective and efficient strategy for learning robust feature extractors, which is essential for few-shot learning. Typical fine-tuning based adaptation paradigm is prone to overfitting in the few-shot learning scenarios and offers little modeling flexibility for learning temporal features in video data. In this work we present the Disentangled-and-Deformable Spatio-Temporal Adapter (D$^2$ST-Adapter), which is a novel adapter tuning framework well-suited for few-shot action recognition due to lightweight design and low parameter-learning overhead. It is designed in a dual-pathway architecture to encode spatial and temporal features in a disentangled manner. In particular, we devise the anisotropic Deformable Spatio-Temporal Attention module as the core component of D$^2$ST-Adapter, which can be tailored with anisotropic sampling densities along spatial and temporal domains to learn spatial and temporal features specifically in corresponding pathways, allowing our D$^2$ST-Adapter to encode features in a global view in 3D spatio-temporal space while maintaining a lightweight design. Extensive experiments with instantiations of our method on both pre-trained ResNet and ViT demonstrate the superiority of our method over state-of-the-art methods for few-shot action recognition. Our method is particularly well-suited to challenging scenarios where temporal dynamics are critical for action recognition.

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