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	A Key Volume Mining Deep Framework for Action Recognition
	Wangjiang Zhu; Jie Hu; Gang Sun; Xudong Cao; Yu Qiao
	2016
会议名称	CVPR2016
会议地点	美国
英文摘要	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%).
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10023]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Wangjiang Zhu,Jie Hu,Gang Sun,et al. A Key Volume Mining Deep Framework for Action Recognition[C]. 见:CVPR2016. 美国.

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