Trear: Transformer-based RGB-D Egocentric Action Recognition

• URL: http://arxiv.org/abs/2101.03904v1
• Date: Tue, 5 Jan 2021 19:59:30 GMT
• Title: Trear: Transformer-based RGB-D Egocentric Action Recognition
• Authors: Xiangyu Li and Yonghong Hou and Pichao Wang and Zhimin Gao and Mingliang Xu and Wanqing Li
• Abstract summary: We propose a textbfTransformer-based RGB-D textbfegocentric textbfaction textbfrecognition framework, called Trear. It consists of two modules, inter-frame attention encoder and mutual-attentional fusion block.
• Score: 38.20137500372927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a \textbf{Tr}ansformer-based RGB-D \textbf{e}gocentric \textbf{a}ction \textbf{r}ecognition framework, called Trear. It consists of two modules, inter-frame attention encoder and mutual-attentional fusion block. Instead of using optical flow or recurrent units, we adopt self-attention mechanism to model the temporal structure of the data from different modalities. Input frames are cropped randomly to mitigate the effect of the data redundancy. Features from each modality are interacted through the proposed fusion block and combined through a simple yet effective fusion operation to produce a joint RGB-D representation. Empirical experiments on two large egocentric RGB-D datasets, THU-READ and FPHA, and one small dataset, WCVS, have shown that the proposed method outperforms the state-of-the-art results by a large margin.

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