MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification

• URL: http://arxiv.org/abs/2002.10979v4
• Date: Tue, 5 May 2020 02:22:42 GMT
• Title: MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification
• Authors: Yushi Lan, Yuan Liu, Maoqing Tian, Xinchi Zhou, Xuesen Zhang, Shuai Yi, Hongsheng Li
• Abstract summary: MagnifierNet is a triple-branch network which accurately mines details from whole to parts. "Semantic Fusion Branch" filters out irrelevant noises by selectively fusing semantic region information sequentially. "Semantic Diversity Loss" removes redundant overlaps across learned semantic representations.
• Score: 38.13515165097505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although person re-identification (ReID) has achieved significant improvement recently by enforcing part alignment, it is still a challenging task when it comes to distinguishing visually similar identities or identifying the occluded person. In these scenarios, magnifying details in each part features and selectively fusing them together may provide a feasible solution. In this work, we propose MagnifierNet, a triple-branch network which accurately mines details from whole to parts. Firstly, the holistic salient features are encoded by a global branch. Secondly, to enhance detailed representation for each semantic region, the "Semantic Adversarial Branch" is designed to learn from dynamically generated semantic-occluded samples during training. Meanwhile, we introduce "Semantic Fusion Branch" to filter out irrelevant noises by selectively fusing semantic region information sequentially. To further improve feature diversity, we introduce a novel loss function "Semantic Diversity Loss" to remove redundant overlaps across learned semantic representations. State-of-the-art performance has been achieved on three benchmarks by large margins. Specifically, the mAP score is improved by 6% and 5% on the most challenging CUHK03-L and CUHK03-D benchmarks.

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