Name
Abstract | ||
|---|---|---|
Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structures with a new segment-based sampling and aggregation module. This unique design enables our TSN to efficiently learn action models by using the whole action videos. The learned models could be easily adapted for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the instantiation of TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on four challenging action recognition benchmarks: HMDB51 (71.0%), UCF101 (94.9%), THUMOS14 (80.1%), and ActivityNet v1.2 (89.6%). Using the proposed RGB difference for motion models, our method can still achieve competitive accuracy on UCF101 (91.0 %) while running at 340 FPS. Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TPAMI.2018.2868668 | IEEE transactions on pattern analysis and machine intelligence |
Keywords | Field | DocType |
Videos,Training,Adaptation models,Analytical models,Visualization,Microsoft Windows,Histograms | Pattern recognition,Computer science,Pooling,Action recognition,Sampling (statistics),RGB color model,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
abs/1705.02953 | 11 | 0162-8828 |
Citations | PageRank | References |
39 | 1.25 | 52 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| LiMin Wang | 1 | 816 | 48.41 |
| Yuanjun Xiong | 2 | 331 | 18.71 |
| Zhe Wang | 3 | 293 | 15.86 |
| Yu Qiao | 4 | 2267 | 152.01 |
| Dahua Lin | 5 | 1117 | 72.62 |
| Xiaoou Tang | 6 | 15728 | 670.19 |
| Luc Van Gool | 7 | 27566 | 1819.51 |