Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond

• URL: http://arxiv.org/abs/2006.06525v3
• Date: Sun, 26 Dec 2021 15:58:45 GMT
• Title: Attentive WaveBlock: Complementarity-enhanced Mutual Networks for Unsupervised Domain Adaptation in Person Re-identification and Beyond
• Authors: Wenhao Wang, Fang Zhao, Shengcai Liao, Ling Shao
• Abstract summary: This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB) AWB can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels. Experiments demonstrate that the proposed method achieves state-of-the-art performance with significant improvements on multiple UDA person re-identification tasks.
• Score: 97.25179345878443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation (UDA) for person re-identification is challenging because of the huge gap between the source and target domain. A typical self-training method is to use pseudo-labels generated by clustering algorithms to iteratively optimize the model on the target domain. However, a drawback to this is that noisy pseudo-labels generally cause trouble in learning. To address this problem, a mutual learning method by dual networks has been developed to produce reliable soft labels. However, as the two neural networks gradually converge, their complementarity is weakened and they likely become biased towards the same kind of noise. This paper proposes a novel light-weight module, the Attentive WaveBlock (AWB), which can be integrated into the dual networks of mutual learning to enhance the complementarity and further depress noise in the pseudo-labels. Specifically, we first introduce a parameter-free module, the WaveBlock, which creates a difference between features learned by two networks by waving blocks of feature maps differently. Then, an attention mechanism is leveraged to enlarge the difference created and discover more complementary features. Furthermore, two kinds of combination strategies, i.e. pre-attention and post-attention, are explored. Experiments demonstrate that the proposed method achieves state-of-the-art performance with significant improvements on multiple UDA person re-identification tasks. We also prove the generality of the proposed method by applying it to vehicle re-identification and image classification tasks. Our codes and models are available at https://github.com/WangWenhao0716/Attentive-WaveBlock.

Related papers

• Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision [16.027843524655516]
  We study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data. Current SFDA methods focus on extracting domain knowledge from the source-trained model but neglects the intrinsic structure of the target domain. We propose a new SFDA framework, called Region-to-Pixel Adaptation Network(RPANet), which learns the region-level and pixel-level discriminative representations through coarse-to-fine self-supervision.
  arXiv  Detail & Related papers  (2023-08-13T02:37:08Z)
• Self-Contrastive Graph Diffusion Network [1.14219428942199]
  We propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN) Our framework consists of two main components: the Attentional Module (AttM) and the Diffusion Module (DiFM) Unlike existing methodologies, SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic drift.
  arXiv  Detail & Related papers  (2023-07-27T04:00:23Z)
• ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation [73.14508297140652]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. We propose an effective method named Proxy-based Mixup training with label refinery ( ProxyMix) Experiments on three 2D image and one 3D point cloud object recognition benchmarks demonstrate that ProxyMix yields state-of-the-art performance for source-free UDA tasks.
  arXiv  Detail & Related papers  (2022-05-29T03:45:00Z)
• Discriminator-Free Generative Adversarial Attack [87.71852388383242]
  Agenerative-based adversarial attacks can get rid of this limitation. ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations. The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
  arXiv  Detail & Related papers  (2021-07-20T01:55:21Z)
• Dual-Stream Reciprocal Disentanglement Learning for Domain Adaption Person Re-Identification [44.80508095481811]
  We propose a novel method named Dual-stream Reciprocal Disentanglement Learning (DRDL), which is quite efficient in learning domain-invariant features. In DRDL, two encoders are first constructed for id-related and id-unrelated feature extractions, which are respectively measured by their associated classifiers. Our proposed method is free from image generation, which not only reduces the computational complexity remarkably, but also removes redundant information from id-related features.
  arXiv  Detail & Related papers  (2021-06-26T03:05:23Z)
• Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation [35.728603077621564]
  Existing Unsupervised Domain Adaptation (UDA) methods presumes source and target domain data to be simultaneously available during training. A pre-trained source model is always considered to be available, even though performing poorly on target due to the well-known domain shift problem. We propose a unified method to tackle adaptive noise filtering and pseudo-label refinement.
  arXiv  Detail & Related papers  (2021-03-29T22:18:34Z)
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search [65.51181219410763]
  One-step methods have been developed to handle pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. We propose a decoupled and memory-reinforced network (DMRNet) to overcome these problems.
  arXiv  Detail & Related papers  (2021-02-22T06:19:45Z)
• Dual-Refinement: Joint Label and Feature Refinement for Unsupervised Domain Adaptive Person Re-Identification [51.98150752331922]
  Unsupervised domain adaptive (UDA) person re-identification (re-ID) is a challenging task due to the missing of labels for the target domain data. We propose a novel approach, called Dual-Refinement, that jointly refines pseudo labels at the off-line clustering phase and features at the on-line training phase. Our method outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-12-26T07:35:35Z)
• Contradictory Structure Learning for Semi-supervised Domain Adaptation [67.89665267469053]
  Current adversarial adaptation methods attempt to align the cross-domain features. Two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. We propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures.
  arXiv  Detail & Related papers  (2020-02-06T22:58:20Z)

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.