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Found 4 papers, 1 papers with code
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
Unlike mainstream approaches using global features for simultaneous multi-task learning of ReID and human parsing, or relying on semantic information for attention guidance, DROP argues that the inferior performance of the former is due to distinct granularity requirements for ReID and human parsing features.
Towards Privacy-Preserving Person Re-identification via Person Identify Shift
In this paper, we aim to develop a technique that can achieve a good trade-off between privacy protection and data usability for person ReID.
Adaptive Discriminative Regularization for Visual Classification
Regarding this hypothesis, we propose a novel regularization to improve discriminative learning.
Hilbert Flattening: a Locality-Preserving Matrix Unfolding Method for Visual Discrimination
Flattening is essential in computer vision by converting multi-dimensional feature maps or images into one-dimensional vectors.
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