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Person Re-identification with Joint
Verification and Identification of
Identity-Attribute Labels
ABSTRACT
One of the major challenges in person Re-Identification (ReID) is the inconsistent visual
appearance of a person. Current works on visual feature and distance metric learning have achieved
significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes,
etc. This makes person ReID among multiple cameras still challenging. This work is motivated to learn midlevel
human attributes which are robust to visual appearance variations and could be used as efficient features
for person matching. We propose a supervised multi-task learning framework which considers attribute
label information with joint identification-verification network to simultaneously learn an attribute-semantic
and identity-discriminative feature representation. Specifically, this framework adopts the part-based deep
neural network and learn three different tasks simultaneously: person identification, person verifications
and attribute identification, so as to discover and capture concurrently complementary discriminative
information about a person image from global and local image features and mid-level attribute features
in one deep neural network.With the multi-task learning architecture, we obtain a discriminative model that
reaches a synergy in distinguishing different person images, as manifested with the competitive accuracy on
three person ReID datasets: Market1501, DukeMTMC-reID and VIPeR.
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