CAST: Cross-Attention in Space and Time for Video Action Recognition
Dongho Lee Jongseo Lee Jinwoo Choi
Recognizing human actions in videos requires spatial and temporal
understanding. Most existing action recognition models lack a balanced
spatio-temporal understanding of videos. In this work, we propose a novel
two-stream architecture, called Cross-Attention in Space and Time (CAST), that
achieves a balanced spatio-temporal understanding of videos using only RGB
input. Our proposed bottleneck cross-attention mechanism enables the spatial
and temporal expert models to exchange information and make synergistic
predictions, leading to improved performance. We validate the proposed method
with extensive experiments on public benchmarks with different characteristics:
EPIC-KITCHENS-100, Something-Something-V2, and Kinetics-400. Our method
consistently shows favorable performance across these datasets, while the
performance of existing methods fluctuates depending on the dataset
characteristics.