FASTER recurrent networks for efficient video classification
- Publication Type:
- Conference Proceeding
- Citation:
- AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, 2020, pp. 13098-13105
- Issue Date:
- 2020-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 7012-Article Text-10241-1-10-20200525.pdf | Published version | 703.88 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to leverage the redundancy between neighboring clips and reduce the computational cost by learning to aggregate the predictions from models of different complexities. The FASTER framework can integrate high quality representations from expensive models to capture subtle motion information and lightweight representations from cheap models to cover scene changes in the video. A new recurrent network (i.e., FAST-GRU) is designed to aggregate the mixture of different representations. Compared with existing approaches, FASTER can reduce the FLOPs by over 10× while maintaining the state-of-the-art accuracy across popular datasets, such as Kinetics, UCF-101 and HMDB-51.
Please use this identifier to cite or link to this item: