Efficient Video Transformers with Spatial-Temporal Token Selection
This work addresses efficiency issues for video recognition tasks, offering a practical improvement for researchers and practitioners using transformer models.
The paper tackles the high computational cost of video transformers by introducing STTS, a token selection framework that dynamically selects informative tokens in spatial and temporal dimensions, achieving similar results on Kinetics-400 with 20% less computation.
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost. In this paper, we present STTS, a token selection framework that dynamically selects a few informative tokens in both temporal and spatial dimensions conditioned on input video samples. Specifically, we formulate token selection as a ranking problem, which estimates the importance of each token through a lightweight scorer network and only those with top scores will be used for downstream evaluation. In the temporal dimension, we keep the frames that are most relevant to the action categories, while in the spatial dimension, we identify the most discriminative region in feature maps without affecting the spatial context used in a hierarchical way in most video transformers. Since the decision of token selection is non-differentiable, we employ a perturbed-maximum based differentiable Top-K operator for end-to-end training. We mainly conduct extensive experiments on Kinetics-400 with a recently introduced video transformer backbone, MViT. Our framework achieves similar results while requiring 20% less computation. We also demonstrate our approach is generic for different transformer architectures and video datasets. Code is available at https://github.com/wangjk666/STTS.