DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition
This addresses robustness and generalization issues in action recognition for computer vision applications, though it appears incremental as it builds on existing Transformer approaches.
The paper tackles the problem of temporal ordering in video action recognition by proposing DirecFormer, a Transformer-based framework with a Directed Attention mechanism, achieving state-of-the-art results on benchmarks like Jester, Kinetics-400, and Something-Something-V2.
Human action recognition has recently become one of the popular research topics in the computer vision community. Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results. However, these methods have suffered some fundamental limitations such as lack of robustness and generalization, e.g., how does the temporal ordering of video frames affect the recognition results? This work presents a novel end-to-end Transformer-based Directed Attention (DirecFormer) framework for robust action recognition. The method takes a simple but novel perspective of Transformer-based approach to understand the right order of sequence actions. Therefore, the contributions of this work are three-fold. Firstly, we introduce the problem of ordered temporal learning issues to the action recognition problem. Secondly, a new Directed Attention mechanism is introduced to understand and provide attentions to human actions in the right order. Thirdly, we introduce the conditional dependency in action sequence modeling that includes orders and classes. The proposed approach consistently achieves the state-of-the-art (SOTA) results compared with the recent action recognition methods, on three standard large-scale benchmarks, i.e. Jester, Kinetics-400 and Something-Something-V2.