A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Fine-grained action recognition datasets exhibit environmental bias, where multiple video sequences are captured from a limited number of environments. Training a model in one environment and deploying in another results in a drop in performance due to an unavoidable domain shift. Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. However, these approaches have not explored the multi-modal nature of video within
doi:10.1109/cvpr42600.2020.00020
dblp:conf/cvpr/MunroD20
fatcat:sevo6h5elbfbzkkla7igpyvm54