Rethinking Person Re-identification from a Projection-on-Prototypes Perspective

• URL: http://arxiv.org/abs/2308.10717v1
• Date: Mon, 21 Aug 2023 13:38:10 GMT
• Title: Rethinking Person Re-identification from a Projection-on-Prototypes Perspective
• Authors: Qizao Wang, Xuelin Qian, Bin Li, Yanwei Fu, Xiangyang Xue
• Abstract summary: Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous development over the past decade. We propose a new baseline ProNet, which innovatively reserves the function of the classifier at the inference stage. Experiments on four benchmarks demonstrate that our proposed ProNet is simple yet effective, and significantly beats previous baselines.
• Score: 84.24742313520811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous development over the past decade. Existing state-of-the-art methods follow an analogous framework to first extract features from the input images and then categorize them with a classifier. However, since there is no identity overlap between training and testing sets, the classifier is often discarded during inference. Only the extracted features are used for person retrieval via distance metrics. In this paper, we rethink the role of the classifier in person Re-ID, and advocate a new perspective to conceive the classifier as a projection from image features to class prototypes. These prototypes are exactly the learned parameters of the classifier. In this light, we describe the identity of input images as similarities to all prototypes, which are then utilized as more discriminative features to perform person Re-ID. We thereby propose a new baseline ProNet, which innovatively reserves the function of the classifier at the inference stage. To facilitate the learning of class prototypes, both triplet loss and identity classification loss are applied to features that undergo the projection by the classifier. An improved version of ProNet++ is presented by further incorporating multi-granularity designs. Experiments on four benchmarks demonstrate that our proposed ProNet is simple yet effective, and significantly beats previous baselines. ProNet++ also achieves competitive or even better results than transformer-based competitors.

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