Deep Concept-wise Temporal Convolutional Networks for Action Localization

26 Aug 2019 Xin Li Tianwei Lin Xiao Liu Chuang Gan WangMeng Zuo Chao Li Xiang Long Dongliang He Fu Li Shilei Wen

Existing action localization approaches adopt shallow temporal convolutional networks (\ie, TCN) on 1D feature map extracted from video frames. In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper