Unsupervised Gait Recognition with Selective Fusion

arXiv:2303.10772v2 Announce Type: replace
Abstract: Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this …

abstract arxiv cs.cv dataset datasets fine-tuning fusion however labeling performance pre-trained model recognition type unsupervised