Proceedings ArticleDOI
A Key Volume Mining Deep Framework for Action Recognition
Wangjiang Zhu,Jie Hu,Gang Sun,Xudong Cao,Yu Qiao +4 more
- pp 1991-1999
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TLDR
A key volume mining deep framework to identify key volumes and conduct classification simultaneously and an effective yet simple "unsupervised key volume proposal" method for high quality volume sampling are proposed.Abstract:
Recently, deep learning approaches have demonstrated remarkable progresses for action recognition in videos. Most existing deep frameworks equally treat every volume i.e. spatial-temporal video clip, and directly assign a video label to all volumes sampled from it. However, within a video, discriminative actions may occur sparsely in a few key volumes, and most other volumes are irrelevant to the labeled action category. Training with a large proportion of irrelevant volumes will hurt performance. To address this issue, we propose a key volume mining deep framework to identify key volumes and conduct classification simultaneously. Specifically, our framework is trained is optimized in an alternative way integrated to the forward and backward stages of Stochastic Gradient Descent (SGD). In the forward pass, our network mines key volumes for each action class. In the backward pass, it updates network parameters with the help of these mined key volumes. In addition, we propose "Stochastic out" to model key volumes from multi-modalities, and an effective yet simple "unsupervised key volume proposal" method for high quality volume sampling. Our experiments show that action recognition performance can be significantly improved by mining key volumes, and we achieve state-of-the-art performance on HMDB51 and UCF101 (93.1%).read more
Citations
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Book ChapterDOI
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
TL;DR: Temporal Segment Networks (TSN) as discussed by the authors combine a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video, which obtains the state-of-the-art performance on the datasets of HMDB51 and UCF101.
Proceedings ArticleDOI
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Zhaofan Qiu,Ting Yao,Tao Mei +2 more
TL;DR: This paper devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 x3 x 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time.
Posted Content
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
TL;DR: Temporal Segment Network (TSN) as discussed by the authors is based on the idea of long-range temporal structure modeling and combines a sparse temporal sampling strategy and video-level supervision to enable efficient and effective learning using the whole action video.
Posted Content
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks
Zhaofan Qiu,Ting Yao,Tao Mei +2 more
TL;DR: P3D ResNet as discussed by the authors proposes a pseudo-3D residual network to exploit all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks.
Proceedings ArticleDOI
Spatiotemporal Multiplier Networks for Video Action Recognition
TL;DR: A general ConvNet architecture for video action recognition based on multiplicative interactions of spacetime features that combines the appearance and motion pathways of a two-stream architecture by motion gating and is trained end-to-end.
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