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GTA: Global Temporal Attention for Video Action Understanding

2020-12-15 18:58:21
Bo He, Xitong Yang, Zuxuan Wu, Hao Chen, Ser-Nam Lim, Abhinav Shrivastava

Abstract

Self-attention learns pairwise interactions via dot products to model long-range dependencies, yielding great improvements for video action recognition. In this paper, we seek a deeper understanding of self-attention for temporal modeling in videos. In particular, we demonstrate that the entangled modeling of spatial-temporal information by flattening all pixels is sub-optimal, failing to capture temporal relationships among frames explicitly. We introduce Global Temporal Attention (GTA), which performs global temporal attention on top of spatial attention in a decoupled manner. Unlike conventional self-attention that computes an instance-specific attention matrix, GTA randomly initializes a global attention matrix that is intended to learn stable temporal structures to generalize across different samples. GTA is further augmented with a cross-channel multi-head fashion to exploit feature interactions for better temporal modeling. We apply GTA not only on pixels but also on semantically similar regions identified automatically by a learned transformation matrix. Extensive experiments on 2D and 3D networks demonstrate that our approach consistently enhances the temporal modeling and provides state-of-the-art performance on three video action recognition datasets.

Abstract (translated)

URL

https://arxiv.org/abs/2012.08510

PDF

https://arxiv.org/pdf/2012.08510.pdf


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