Name
Title | ||
|---|---|---|
Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition. |
Abstract | ||
|---|---|---|
In this report, our approach to tackling the task of ActivityNet 2018 Kinetics-600 challenge is described in detail. Though spatial-temporal modelling methods, which adopt either such end-to-end framework as I3D cite{i3d} or two-stage frameworks (i.e., CNN+RNN), have been proposed in existing state-of-the-arts for this task, video modelling is far from being well solved. In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding. Besides, given that multi-modal information is contained in video source, we manage to integrate both early-fusion and later-fusion strategy of multi-modal information via our proposed improved temporal Xception network (iTXN) for video understanding. Our StNet RGB single model achieves 78.99% top-1 precision in the Kinetics-600 validation set and that of our improved temporal Xception network which integrates RGB, flow and audio modalities is up to 82.35%. After model ensemble, we achieve top-1 precision as high as 85.0% on the validation set and rank No.1 among all submissions. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Modalities,Pattern recognition,Computer science,Action recognition,Fusion,RGB color model,Artificial intelligence,Machine learning,Modal |
DocType | Volume | Citations |
Journal | abs/1806.10319 | 1 |
PageRank | References | Authors |
0.42 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He, D. | 1 | 33 | 13.67 |
| Fu Li | 2 | 98 | 19.30 |
| Qijie Zhao | 3 | 11 | 3.30 |
| Xiang Long | 4 | 30 | 10.70 |
| Yi Fu | 5 | 5 | 1.53 |
| Shilei Wen | 6 | 79 | 13.59 |