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Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition
In this paper, we propose an end-to-end Spatial-Temporal Attention Residual Temporal Convolutional Network (STA-Res-TCN) for skeleton-based dynamic hand gesture recognition, which learns different levels of attention and assigns them to each spatial-temporal feature extracted by the convolution filters at each time step.
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition
Based on the findings from the model interpretation analysis, we propose a targeted refinement technique, which can generalize to various other recognition models.
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