Domain generalization Person Re-identification on Attention-aware multi-operation strategery

• URL: http://arxiv.org/abs/2210.10409v1
• Date: Wed, 19 Oct 2022 09:18:46 GMT
• Title: Domain generalization Person Re-identification on Attention-aware multi-operation strategery
• Authors: Yingchun Guo, Huan He, Ye Zhu, Yang Yu
• Abstract summary: Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization. In the existing DG Re-ID methods, invariant operations are effective in extracting domain generalization features. An Attention-aware Multi-operation Strategery (AMS) for DG Re-ID is proposed to extract more generalized features.
• Score: 8.90472129039969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization person re-identification (DG Re-ID) aims to directly deploy a model trained on the source domain to the unseen target domain with good generalization, which is a challenging problem and has practical value in a real-world deployment. In the existing DG Re-ID methods, invariant operations are effective in extracting domain generalization features, and Instance Normalization (IN) or Batch Normalization (BN) is used to alleviate the bias to unseen domains. Due to domain-specific information being used to capture discriminability of the individual source domain, the generalized ability for unseen domains is unsatisfactory. To address this problem, an Attention-aware Multi-operation Strategery (AMS) for DG Re-ID is proposed to extract more generalized features. We investigate invariant operations and construct a multi-operation module based on IN and group whitening (GW) to extract domain-invariant feature representations. Furthermore, we analyze different domain-invariant characteristics, and apply spatial attention to the IN operation and channel attention to the GW operation to enhance the domain-invariant features. The proposed AMS module can be used as a plug-and-play module to incorporate into existing network architectures. Extensive experimental results show that AMS can effectively enhance the model's generalization ability to unseen domains and significantly improves the recognition performance in DG Re-ID on three protocols with ten datasets.

Related papers

• Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
  Unsupervised domain generalization is fast gaining attention but is still far from well-studied. Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features. DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
  arXiv  Detail & Related papers  (2024-07-10T11:11:36Z)
• Adaptive Domain Generalization via Online Disagreement Minimization [17.215683606365445]
  Domain Generalization aims to safely transfer a model to unseen target domains. AdaODM adaptively modifies the source model at test time for different target domains. Results show AdaODM stably improves the generalization capacity on unseen domains.
  arXiv  Detail & Related papers  (2022-08-03T11:51:11Z)
• Compound Domain Generalization via Meta-Knowledge Encoding [55.22920476224671]
  We introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions. We harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space. Experiments on four standard Domain Generalization benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision.
  arXiv  Detail & Related papers  (2022-03-24T11:54:59Z)
• META: Mimicking Embedding via oThers' Aggregation for Generalizable Person Re-identification [68.39849081353704]
  Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time. This paper presents a new approach called Mimicking Embedding via oThers' Aggregation (META) for DG ReID.
  arXiv  Detail & Related papers  (2021-12-16T08:06:50Z)
• 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)
• Exploiting Domain-Specific Features to Enhance Domain Generalization [10.774902700296249]
  Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. Prior DG approaches have focused on extracting domain-invariant information across sources to generalize on target domains. We propose meta-Domain Specific-Domain Invariant (mD) - a novel theoretically sound framework.
  arXiv  Detail & Related papers  (2021-10-18T15:42:39Z)
• COLUMBUS: Automated Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption [12.555885317622131]
  We address the challenging domain generalization problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without exposure to their data. We propose Columbus, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multi-level representations of the data.
  arXiv  Detail & Related papers  (2021-09-09T14:52:05Z)
• Adaptive Domain-Specific Normalization for Generalizable Person Re-Identification [81.30327016286009]
  We propose a novel adaptive domain-specific normalization approach (AdsNorm) for generalizable person Re-ID. In this work, we propose a novel adaptive domain-specific normalization approach (AdsNorm) for generalizable person Re-ID.
  arXiv  Detail & Related papers  (2021-05-07T02:54:55Z)
• Dual Distribution Alignment Network for Generalizable Person Re-Identification [174.36157174951603]
  Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID) We present a Dual Distribution Alignment Network (DDAN) which handles this challenge by selectively aligning distributions of multiple source domains. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark.
  arXiv  Detail & Related papers  (2020-07-27T00:08:07Z)

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.