2,271 research outputs found
Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
Due to the modality gap between visible and infrared images with high visual
ambiguity, learning \textbf{diverse} modality-shared semantic concepts for
visible-infrared person re-identification (VI-ReID) remains a challenging
problem. Body shape is one of the significant modality-shared cues for VI-ReID.
To dig more diverse modality-shared cues, we expect that erasing
body-shape-related semantic concepts in the learned features can force the ReID
model to extract more and other modality-shared features for identification. To
this end, we propose shape-erased feature learning paradigm that decorrelates
modality-shared features in two orthogonal subspaces. Jointly learning
shape-related feature in one subspace and shape-erased features in the
orthogonal complement achieves a conditional mutual information maximization
between shape-erased feature and identity discarding body shape information,
thus enhancing the diversity of the learned representation explicitly.
Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate
the effectiveness of our method.Comment: CVPR 202
A Novel Multi-Color Feature Selection Method for Person Re-identification
In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances
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