Name
Title | ||
|---|---|---|
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations |
Abstract | ||
|---|---|---|
Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activities, which are not designed for multi-label activities. We introduce an approach to multi-label activity recognition that extracts independent feature descriptors for each activity and learns activity correlations. This structure can be trained end-to-end and plugged into any existing network structures for video classification. Our method outperformed state-of-the-art approaches on four multi-label activity recognition datasets. To better understand the activity-specific features that the system generated, we visualized these activity-specific features in the Charades dataset. The code will be released later. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.01439 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | Volume | ISSN |
Conference | 2021 | 1063-6919 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyi Zhang | 1 | 29 | 6.40 |
| Xinyu Li | 2 | 381 | 65.75 |
| Ivan Marsic | 3 | 716 | 91.96 |