794 citations · 4.8k across the 152 of their papers we have counts for
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PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
Xingcheng Zhang, Zhizhong Li, Chen Change Loy +1
A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, h…
Deep Markov Random Field for Image Modeling
Zhirong Wu, Dahua Lin, Xiaoou Tang
Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact…
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Limin Wang, Yuanjun Xiong, Zhe Wang +4
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…
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
Yuanjun Xiong, Limin Wang, Zhe Wang +7
This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segm…