Part-Aware Transformer for Generalizable Person Re-identification

• URL: http://arxiv.org/abs/2308.03322v2
• Date: Mon, 18 Sep 2023 08:18:31 GMT
• Title: Part-Aware Transformer for Generalizable Person Re-identification
• Authors: Hao Ni, Yuke Li, Lianli Gao, Heng Tao Shen, Jingkuan Song
• Abstract summary: Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. We propose a pure Transformer model (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL) This proxy task allows the model to learn generic features because it only cares about the visual similarity of the parts regardless of the ID labels.
• Score: 138.99827526048205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Vision Transformer usually yields better generalization ability than common CNN networks under distribution shifts. However, Transformer-based ReID models inevitably over-fit to domain-specific biases due to the supervised learning strategy on the source domain. We observe that while the global images of different IDs should have different features, their similar local parts (e.g., black backpack) are not bounded by this constraint. Motivated by this, we propose a pure Transformer model (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local visual information shared by different IDs. This proxy task allows the model to learn generic features because it only cares about the visual similarity of the parts regardless of the ID labels, thus alleviating the side effect of domain-specific biases. Based on the local similarity obtained in CSL, a Part-guided Self-Distillation (PSD) is proposed to further improve the generalization of global features. Our method achieves state-of-the-art performance under most DG ReID settings. Under the Market$\to$Duke setting, our method exceeds state-of-the-art by 10.9% and 12.8% in Rank1 and mAP, respectively. The code is available at https://github.com/liyuke65535/Part-Aware-Transformer.

Related papers

• Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification [63.147482497821166]
  We first explore the influence of global and local features of ViT and then propose a novel Global-Local Transformer (GLTrans) for high-performance object Re-ID. Our proposed method achieves superior performance on four object Re-ID benchmarks.
  arXiv  Detail & Related papers  (2024-04-23T12:42:07Z)
• Learning Invariance from Generated Variance for Unsupervised Person Re-identification [15.096776375794356]
  We propose to replace traditional data augmentation with a generative adversarial network (GAN) A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T15:40:14Z)
• Style Interleaved Learning for Generalizable Person Re-identification [69.03539634477637]
  We propose a novel style interleaved learning (IL) framework for DG ReID training. Unlike conventional learning strategies, IL incorporates two forward propagations and one backward propagation for each iteration. We show that our model consistently outperforms state-of-the-art methods on large-scale benchmarks for DG ReID.
  arXiv  Detail & Related papers  (2022-07-07T07:41:32Z)
• HAT: Hierarchical Aggregation Transformers for Person Re-identification [87.02828084991062]
  We take advantages of both CNNs and Transformers for image-based person Re-ID with high performance. Work is the first to take advantages of both CNNs and Transformers for image-based person Re-ID.
  arXiv  Detail & Related papers  (2021-07-13T09:34:54Z)
• Style Normalization and Restitution for Generalizable Person Re-identification [89.482638433932]
  We design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. We propose a simple yet effective Style Normalization and Restitution (SNR) module. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks.
  arXiv  Detail & Related papers  (2020-05-22T07:15:10Z)
• Learning Meta Face Recognition in Unseen Domains [74.69681594452125]
  We propose a novel face recognition method via meta-learning named Meta Face Recognition (MFR) MFR synthesizes the source/target domain shift with a meta-optimization objective. We propose two benchmarks for generalized face recognition evaluation.
  arXiv  Detail & Related papers  (2020-03-17T14:10:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.