1.3k citations · 3.2k across the 86 of their papers we have counts for
5 papers · 1 filter
Learning to Point and Count
Jie Shao, Dequan Wang, Xiangyang Xue +1
This paper proposes the problem of point-and-count as a test case to break the what-and-where deadlock. Different from the traditional detection problem, the goal is to discover ke…
Fusing Multi-Stream Deep Networks for Video Classification
Zuxuan Wu, Yu-Gang Jiang, Xi Wang +3
This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information…
Evaluating Two-Stream CNN for Video Classification
Hao Ye, Zuxuan Wu, Rui-Wei Zhao +3
Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of th…
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
Zuxuan Wu, Xi Wang, Yu-Gang Jiang +2
Classifying videos according to content semantics is an important problem with a wide range of applications. In this paper, we propose a hybrid deep learning framework for video cl…
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
Yu-Gang Jiang, Zuxuan Wu, Jun Wang +2
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event. Alth…