Meta Batch-Instance Normalization for Generalizable Person Re-Identification

• URL: http://arxiv.org/abs/2011.14670v2
• Date: Mon, 29 Mar 2021 17:38:27 GMT
• Title: Meta Batch-Instance Normalization for Generalizable Person Re-Identification
• Authors: Seokeon Choi, Taekyung Kim, Minki Jeong, Hyoungseob Park, Changick Kim
• Abstract summary: We propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN) Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand. Our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem.
• Score: 36.74050132062411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://github.com/bismex/MetaBIN.

Related papers

• Deep Multimodal Fusion for Generalizable Person Re-identification [15.250738959921872]
  DMF is a Deep Multimodal Fusion network for the general scenarios on person re-identification task. Rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. A realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world.
  arXiv  Detail & Related papers  (2022-11-02T07:42:48Z)
• 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)
• DEX: Domain Embedding Expansion for Generalized Person Re-identification [40.275824026850245]
  Domain Embedding Expansion (DEX) module dynamically manipulates and augments deep features based on person and domain labels during training. DEXLite, applying negative sampling techniques to scale to larger datasets and reduce memory usage for multi-branch networks. Our proposed DEX and DEXLite can be combined with many existing methods, Bag-of-Tricks, the Multi-Granularity Network (MGN), and Part-Based Convolutional Baseline (PCB)
  arXiv  Detail & Related papers  (2021-10-21T18:21:22Z)
• 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)
• Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification [59.326456778057384]
  We propose the Memory-based Multi-Source Meta-Learning framework to train a generalizable model for unseen domains. We also present a meta batch normalization layer (MetaBN) to diversify meta-test features. Experiments demonstrate that our M$3$L can effectively enhance the generalization ability of the model for unseen domains.
  arXiv  Detail & Related papers  (2020-12-01T11:38:16Z)
• Improving Generalization in Meta-learning via Task Augmentation [69.83677015207527]
  We propose two task augmentation methods, including MetaMix and Channel Shuffle. Both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets.
  arXiv  Detail & Related papers  (2020-07-26T01:50:42Z)
• 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)

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.