Name
Title | ||
|---|---|---|
Unsupervised Representation Learning With Long-Term Dynamics for Skeleton Based Action Recognition. |
Abstract | ||
|---|---|---|
In recent years, skeleton based action recognition is becoming an increasingly attractive alternative to existing video-based approaches, beneficial from its robust and comprehensive 3D information. In this paper, we explore an unsupervised representation learning approach for the first time to capture the long-term global motion dynamics in skeleton sequences. We design a conditional skeleton inpainting architecture for learning a fixed-dimensional representation, guided by additional adversarial training strategies. We quantitatively evaluate the effectiveness of our learning approach on three well-established action recognition datasets. Experimental results show that our learned representation is discriminative for classifying actions and can substantially reduce the sequence inpainting errors. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Computer science,Action recognition,Artificial intelligence,Skeleton (computer programming),Machine learning,Feature learning |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nenggan Zheng | 1 | 141 | 24.83 |
| Jun Wen | 2 | 5 | 1.76 |
| Risheng Liu | 3 | 29 | 5.81 |
| Liangqu Long | 4 | 2 | 0.37 |
| Jianhua Dai | 5 | 10 | 4.59 |
| Z Gong | 6 | 6 | 1.79 |