4 accepted papers
This paper aims to learn a domain-generalizable (DG) person re-identification (ReID) representation from large-scale videos without any annotation. Prior DG ReID methods employ limited labeled data for training due to the high cost of annotation, which restricts further advances. To overcome the bar…
Person re-identification (re-ID) aims to match pedestrian images from non-overlapping cameras. It is a challenging task because of the feature misalignment problem caused by occlusion. In this paper, inspired by the coarse-to-fine nature of human perception, we propose a novel Progressive-Granularit…
"Current person image retrieval methods have achieved great improvements in accuracy metrics. However, they rarely describe the reliability of the prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL) method to remedy this issue. UAL aims at providing reliability-aware prediction…
This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatia…