TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2412.04073v1
• Date: Thu, 05 Dec 2024 11:11:39 GMT
• Title: TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation
• Authors: A. Enes Doruk, Erhan Oztop, Hasan F. Ates,
• Abstract summary: Unsupervised Domain Adaptation (UDA) aims to utilize labeled data from a source domain to solve tasks in an unlabeled target domain. Traditional CNN-based methods struggle to fully capture complex domain relationships. We propose a novel UDA approach leveraging the Swin Transformer with three key modules.
• Score: 0.3277163122167433
• License:
• Abstract: Unsupervised Domain Adaptation (UDA) aims to utilize labeled data from a source domain to solve tasks in an unlabeled target domain, often hindered by significant domain gaps. Traditional CNN-based methods struggle to fully capture complex domain relationships, motivating the shift to vision transformers like the Swin Transformer, which excel in modeling both local and global dependencies. In this work, we propose a novel UDA approach leveraging the Swin Transformer with three key modules. A Graph Domain Discriminator enhances domain alignment by capturing inter-pixel correlations through graph convolutions and entropy-based attention differentiation. An Adaptive Double Attention module combines Windows and Shifted Windows attention with dynamic reweighting to align long-range and local features effectively. Finally, a Cross-Feature Transform modifies Swin Transformer blocks to improve generalization across domains. Extensive benchmarks confirm the state-of-the-art performance of our versatile method, which requires no task-specific alignment modules, establishing its adaptability to diverse applications.

Related papers

• Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation [51.10389829070684]
  Domain gap can cause discrepancies in self-attention. Due to this gap, the transformer attends to spurious regions or pixels, which deteriorates accuracy on the target domain. We propose adaptation on attention maps with cross-domain attention layers.
  arXiv  Detail & Related papers  (2022-11-27T02:40:33Z)
• Domain Adaptation for Object Detection using SE Adaptors and Center Loss [0.0]
  We introduce an unsupervised domain adaptation method built on the foundation of faster-RCNN to prevent drops in performance due to domain shift. We also introduce a family of adaptation layers that leverage the squeeze excitation mechanism called SE Adaptors to improve domain attention. Finally, we incorporate a center loss in the instance and image level representations to improve the intra-class variance.
  arXiv  Detail & Related papers  (2022-05-25T17:18:31Z)
• Safe Self-Refinement for Transformer-based Domain Adaptation [73.8480218879]
  Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a large domain gap lies between the source and target domains. We propose a novel solution named SSRT (Safe Self-Refinement for Transformer-based domain adaptation), which brings improvement from two aspects.
  arXiv  Detail & Related papers  (2022-04-16T00:15:46Z)
• Towards Unsupervised Domain Adaptation via Domain-Transformer [0.0]
  We propose the Domain-Transformer (DoT) for Unsupervised Domain Adaptation (UDA) DoT integrates the CNN-backbones and the core attention mechanism of Transformers from a new perspective. It achieves the local semantic consistency across domains, where the domain-level attention and manifold regularization are explored.
  arXiv  Detail & Related papers  (2022-02-24T02:30:15Z)
• CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation [44.06904757181245]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to a different unlabeled target domain. One fundamental problem for the category level based UDA is the production of pseudo labels for samples in target domain. We design a two-way center-aware labeling algorithm to produce pseudo labels for target samples. Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment.
  arXiv  Detail & Related papers  (2021-09-13T17:59:07Z)
• Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers [141.70707071815653]
  We propose a novel Sequence Feature Alignment (SFA) method that is specially designed for the adaptation of detection transformers. SFA consists of a domain query-based feature alignment (DQFA) module and a token-wise feature alignment (TDA) module. Experiments on three challenging benchmarks show that SFA outperforms state-of-the-art domain adaptive object detection methods.
  arXiv  Detail & Related papers  (2021-07-27T07:17:12Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Transformer-Based Source-Free Domain Adaptation [134.67078085569017]
  We study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. We propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA.
  arXiv  Detail & Related papers  (2021-05-28T23:06:26Z)
• Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks [3.756448228784421]
  We present a novel "deep adversarial transition learning" (DATL) framework that bridges the domain gap. We construct variational auto-encoders (VAEs) for the two domains, and form bidirectional transitions by cross-grafting the VAEs' decoder stacks. generative adversarial networks (GAN) are employed for domain adaptation, mapping the target domain data to the known label space of the source domain.
  arXiv  Detail & Related papers  (2020-09-25T04:25:27Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.