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Article overview
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Temporal Segment Networks: Towards Good Practices for Deep Action Recognition | | Limin Wang
; Yuanjun Xiong
; Zhe Wang
; Yu Qiao
; Dahua Lin
; Xiaoou Tang
; Luc Van Gool
; | | Date: |
Tue, 2 Aug 2016 15:06:50 GMT (1805kb,D) | | Abstract: | Deep convolutional networks have achieved great success for visual
recognition in still images. However, for action recognition in videos, the
advantage over traditional methods is not so evident. This paper aims to
discover the principles to design effective ConvNet architectures for action
recognition in videos and learn these models given limited training samples.
Our first contribution is temporal segment network (TSN), a novel framework for
video-based action recognition. which is based on the idea of long-range
temporal structure modeling. It combines a sparse temporal sampling strategy
and video-level supervision to enable efficient and effective learning using
the whole action video. The other contribution is our study on a series of good
practices in learning ConvNets on video data with the help of temporal segment
network. Our approach obtains the state-the-of-art performance on the datasets
of HMDB51 ( $ 69.4\% $) and UCF101 ($ 94.2\% $). We also visualize the learned
ConvNet models, which qualitatively demonstrates the effectiveness of temporal
segment network and the proposed good practices. | | Source: | arXiv, 1608.0859 | | Services: | Forum | Review | PDF | Favorites | |
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