AIM: Adapting Image Models for Efficient Video Action Recognition
This work addresses the efficiency challenge in adapting powerful image models for video tasks, which is incremental but offers practical benefits for researchers and practitioners in computer vision.
The authors tackled the problem of computationally expensive full fine-tuning of pre-trained image models for video action recognition by proposing AIM, a method that freezes the image model and adds lightweight adapters for spatial, temporal, and joint adaptation, achieving competitive or better performance with substantially fewer tunable parameters on four benchmarks.
Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have demonstrated exceptional transferability. In this work, we propose a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding. By freezing the pre-trained image model and adding a few lightweight Adapters, we introduce spatial adaptation, temporal adaptation and joint adaptation to gradually equip an image model with spatiotemporal reasoning capability. We show that our proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks. Thanks to its simplicity, our method is also generally applicable to different image pre-trained models, which has the potential to leverage more powerful image foundation models in the future. The project webpage is \url{https://adapt-image-models.github.io/}.