Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
文献类型：会议论文
| 作者 | Limin Wang; Yuanjun Xiong; Zhe Wang; Yu Qiao; Dahua Lin; Xiaoou Tang; Luc Van Gool |
| 出版日期 | 2016 |
| 会议名称 | ECCV2016 |
| 会议地点 | 荷兰阿姆斯特丹 |
| 英文摘要 | 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 (Models and code at https://github.com/yjxiong/temporal-segment-networks). |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10027]  |
| 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 | Limin Wang,Yuanjun Xiong,Zhe Wang,et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition[C]. 见:ECCV2016. 荷兰阿姆斯特丹. |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。