Human Action Recognition: Pose-Based Attention Draws Focus to Hands

Fabien Baradel, Christian Wolf, Julien Mille; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 604-613

We propose a new spatio-temporal attention based mechanism for human action recognition able to automatically attend to most important human hands and detect the most discriminative moments in an action. Attention is handled in a recurrent manner employing Recurrent Neural Network (RNN) and is fully-differentiable. In contrast to standard soft-attention based mechanisms, our approach does not use the hidden RNN state as input to the attention model. Instead, attention distributions are drawn using external in- formation: human articulated pose. We performed an ex- tensive ablation study to show the strengths of this approach and we particularly studied the conditioning aspect of the attention mechanism. We evaluate the method on the largest currently available human action recognition dataset, NTU- RGB+D, and report state-of-the-art results. Another ad- vantage of our model are certains aspects of explanability, as the spatial and temporal attention distributions at test time allow to study and verify on which parts of the input data the method focuses.