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Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification
2019
Applied Sciences
In this work, we focus on the misalignment problem in person re-identification. Human body parts commonly contain discriminative local representations relevant with identity recognition. However, the representations are easily affected by misalignment that is due to varying poses or poorly detected bounding boxes. We thus present a two-branch Deep Joint Learning (DJL) network, where the local branch generates misalignment robust representations by pooling the features around the body parts,
doi:10.3390/app9112255
fatcat:m6h2263qandbtobhltog4gvwaq