IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID

• URL: http://arxiv.org/abs/2108.02413v1
• Date: Thu, 5 Aug 2021 07:19:46 GMT
• Title: IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
• Authors: Yongxing Dai, Jun Liu, Yifan Sun, Zekun Tong, Chi Zhang, Ling-Yu Duan
• Abstract summary: We argue that the bridging between the source and target domains can be utilized to tackle the UDA re-ID task. We propose an Intermediate Domain Module (IDM) to generate intermediate domains' representations on-the-fly. Our proposed method outperforms the state-of-the-arts by a large margin in all the common UDA re-ID tasks.
• Score: 58.46907388691056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the labeled source domain's knowledge to improve the model's discriminability on the unlabeled target domain. From a novel perspective, we argue that the bridging between the source and target domains can be utilized to tackle the UDA re-ID task, and we focus on explicitly modeling appropriate intermediate domains to characterize this bridging. Specifically, we propose an Intermediate Domain Module (IDM) to generate intermediate domains' representations on-the-fly by mixing the source and target domains' hidden representations using two domain factors. Based on the "shortest geodesic path" definition, i.e., the intermediate domains along the shortest geodesic path between the two extreme domains can play a better bridging role, we propose two properties that these intermediate domains should satisfy. To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors. The bridge losses aim at guiding the distribution of appropriate intermediate domains to keep the right distance to the source and target domains. The diversity loss serves as a regularization to prevent the generated intermediate domains from being over-fitting to either of the source and target domains. Our proposed method outperforms the state-of-the-arts by a large margin in all the common UDA re-ID tasks, and the mAP gain is up to 7.7% on the challenging MSMT17 benchmark. Code is available at https://github.com/SikaStar/IDM.

Related papers

• Adversarial Bi-Regressor Network for Domain Adaptive Regression [52.5168835502987]
  It is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross-domain regression model.
  arXiv  Detail & Related papers  (2022-09-20T18:38:28Z)
• Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains [63.23373987549485]
  Cross-domain person re-identification (re-ID) aims to transfer the identity-discriminative knowledge from the source to the target domain. We propose an Intermediate Domain Module (IDM) and a Mirrors Generation Module (MGM) IDM generates multiple intermediate domains by mixing the hidden-layer features from source and target domains. MGM is introduced by mapping the features into the IDM-generated intermediate domains without changing their original identity.
  arXiv  Detail & Related papers  (2022-03-03T12:44:56Z)
• Domain-Augmented Domain Adaptation [5.292532408558036]
  Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain. We propose the domain-augmented domain adaptation (DADA) to generate pseudo domains that have smaller discrepancies with the target domain. We conduct extensive experiments with the state-of-the-art domain adaptation methods on four benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-21T05:42:02Z)
• ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation [10.65673380743972]
  ConDA is a concatenation-based domain adaptation framework for LiDAR segmentation. We propose an anti-aliasing regularizer and an entropy aggregator to reduce the negative effect caused by the aliasing artifacts and noisy pseudo labels.
  arXiv  Detail & Related papers  (2021-11-30T09:53:24Z)
• Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification [57.92800886719651]
  Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. We propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification.
  arXiv  Detail & Related papers  (2021-06-16T07:29:27Z)
• Domain Conditioned Adaptation Network [90.63261870610211]
  We propose a Domain Conditioned Adaptation Network (DCAN) to excite distinct convolutional channels with a domain conditioned channel attention mechanism. This is the first work to explore the domain-wise convolutional channel activation for deep DA networks.
  arXiv  Detail & Related papers  (2020-05-14T04:23:24Z)
• Unsupervised Domain Adaptation with Progressive Domain Augmentation [34.887690018011675]
  We propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain, progressively augments the source domain and bridges the source-target domain divergence. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-03T18:45:39Z)
• MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation [58.38749495295393]
  Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Recent multi-source domain adaptation (MDA) methods do not consider the pixel-level alignment between sources and target. We propose a novel MDA framework to address these challenges.
  arXiv  Detail & Related papers  (2020-02-19T21:22:00Z)

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.