Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization

• URL: http://arxiv.org/abs/2001.08680v3
• Date: Sat, 18 Jul 2020 15:37:06 GMT
• Title: Rethinking the Distribution Gap of Person Re-identification with Camera-based Batch Normalization
• Authors: Zijie Zhuang, Longhui Wei, Lingxi Xie, Tianyu Zhang, Hengheng Zhang, Haozhe Wu, Haizhou Ai, and Qi Tian
• Abstract summary: This paper rethinks the working mechanism of conventional ReID approaches. We force the image data of all cameras to fall onto the same subspace, so that the distribution gap between any camera pair is largely shrunk. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach.
• Score: 90.9485099181197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fundamental difficulty in person re-identification (ReID) lies in learning the correspondence among individual cameras. It strongly demands costly inter-camera annotations, yet the trained models are not guaranteed to transfer well to previously unseen cameras. These problems significantly limit the application of ReID. This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution. With an effective operator named Camera-based Batch Normalization (CBN), we force the image data of all cameras to fall onto the same subspace, so that the distribution gap between any camera pair is largely shrunk. This alignment brings two benefits. First, the trained model enjoys better abilities to generalize across scenarios with unseen cameras as well as transfer across multiple training sets. Second, we can rely on intra-camera annotations, which have been undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at https://github.com/automan000/Camera-based-Person-ReID.

Related papers

• Camera-aware Label Refinement for Unsupervised Person Re-identification [19.099192313056296]
  Unsupervised person re-identification aims to retrieve images of a specified person without identity labels. Recent unsupervised Re-ID approaches adopt clustering-based methods to measure cross-camera feature similarity. We introduce a textbfCamera-textbfAware textbfLabel textbfRefinement(CALR) framework that reduces camera discrepancy by clustering intra-camera similarity.
  arXiv  Detail & Related papers  (2024-03-25T06:22:27Z)
• Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification [6.477096324232456]
  Cross-camera images are prone to being recognized as different IDs simply by camera style. This paper studies person re-ID under such isolated camera supervised (ISCS) setting.
  arXiv  Detail & Related papers  (2023-11-02T11:32:40Z)
• Handling Label Uncertainty for Camera Incremental Person Re-Identification [17.5026399908583]
  Incremental learning for person re-identification (ReID) aims to develop models that can be trained with a continuous data stream. New data collected from new cameras may probably contain an unknown proportion of identities seen before. We propose a novel framework ExtendOVA to handle the class overlap issue.
  arXiv  Detail & Related papers  (2022-10-17T02:59:54Z)
• Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes [70.30052164401178]
  Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. ICS-DS Re-ID uses cross-camera unpaired data with intra-camera identity labels for training. Cross-camera feature prediction method to mine cross-camera self supervision information. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme.
  arXiv  Detail & Related papers  (2021-07-29T11:27:50Z)
• Intra-Inter Camera Similarity for Unsupervised Person Re-Identification [50.85048976506701]
  We study a novel intra-inter camera similarity for pseudo-label generation. We train our re-id model in two stages with intra-camera and inter-camera pseudo-labels, respectively. This simple intra-inter camera similarity produces surprisingly good performance on multiple datasets.
  arXiv  Detail & Related papers  (2021-03-22T08:29:04Z)
• Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification [60.36551512902312]
  unsupervised person re-identification (re-ID) aims to learn discriminative models with unlabeled data. One popular method is to obtain pseudo-label by clustering and use them to optimize the model. In this paper, we propose a unified framework to solve both problems.
  arXiv  Detail & Related papers  (2021-03-08T09:13:06Z)
• Camera-aware Proxies for Unsupervised Person Re-Identification [60.26031011794513]
  This paper tackles the purely unsupervised person re-identification (Re-ID) problem that requires no annotations. We propose to split each single cluster into multiple proxies and each proxy represents the instances coming from the same camera. Based on the camera-aware proxies, we design both intra- and inter-camera contrastive learning components for our Re-ID model.
  arXiv  Detail & Related papers  (2020-12-19T12:37:04Z)
• Intra-Camera Supervised Person Re-Identification [87.88852321309433]
  We propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process. We formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method for Intra-Camera Supervised (ICS) person re-id.
  arXiv  Detail & Related papers  (2020-02-12T15:26:33Z)

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.