Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption

• URL: http://arxiv.org/abs/2203.00672v1
• Date: Tue, 1 Mar 2022 18:46:32 GMT
• Title: Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption
• Authors: Ke Han, Chenyang Si, Yan Huang, Liang Wang, Tieniu Tan
• Abstract summary: Batch Norm Test-time Adaption (BNTA) is a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. BNTA explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain.
• Score: 63.7424680360004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the state-of-the-art methods.

Related papers

• Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay [23.871860648919593]
  We introduce an algorithm for tackling the problem of unsupervised domain adaptation (UDA) in continual learning (CL) scenarios. Our solution is based on stabilizing the learned internal distribution to enhances the model generalization on new domains. We leverage experience replay to overcome the problem of catastrophic forgetting, where the model loses previously acquired knowledge when learning new tasks.
  arXiv  Detail & Related papers  (2024-01-31T05:09:14Z)
• Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization [39.14048972373775]
  Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. We propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer.
  arXiv  Detail & Related papers  (2023-12-15T19:22:21Z)
• Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features [37.518025833882334]
  Open Domain Generalization (ODG) is a challenging task as it deals with distribution shifts and category shifts. Previous work has used multiple source-specific networks, which involve a high cost. This paper proposes a method that can handle ODG using only a single network.
  arXiv  Detail & Related papers  (2023-12-08T16:22:10Z)
• Patch-aware Batch Normalization for Improving Cross-domain Robustness [55.06956781674986]
  Cross-domain tasks present a challenge in which the model's performance will degrade when the training set and the test set follow different distributions. We propose a novel method called patch-aware batch normalization (PBN) By exploiting the differences between local patches of an image, our proposed PBN can effectively enhance the robustness of the model's parameters.
  arXiv  Detail & Related papers  (2023-04-06T03:25:42Z)
• Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation [13.260109561599904]
  Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to an unlabeled heterogeneous target domain. We propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation.
  arXiv  Detail & Related papers  (2022-09-16T13:13:50Z)
• Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework [50.88463458896428]
  Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. Existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. We propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module.
  arXiv  Detail & Related papers  (2021-11-30T02:35:51Z)
• Calibrated Feature Decomposition for Generalizable Person Re-Identification [82.64133819313186]
  Calibrated Feature Decomposition (CFD) module focuses on improving the generalization capacity for person re-identification. A calibrated-and-standardized Batch normalization (CSBN) is designed to learn calibrated person representation.
  arXiv  Detail & Related papers  (2021-11-27T17:12:43Z)
• Unsupervised and self-adaptative techniques for cross-domain person re-identification [82.54691433502335]
  Person Re-Identification (ReID) across non-overlapping cameras is a challenging task. Unsupervised Domain Adaptation (UDA) is a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. In this paper, we propose a novel UDA-based ReID method that takes advantage of triplets of samples created by a new offline strategy.
  arXiv  Detail & Related papers  (2021-03-21T23:58:39Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.