Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation

• URL: http://arxiv.org/abs/2212.04145v1
• Date: Thu, 8 Dec 2022 08:56:02 GMT
• Title: Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation
• Authors: Yulu Gan, Xianzheng Ma, Yihang Lou, Yan Bai, Renrui Zhang, Nian Shi, Lin Luo
• Abstract summary: Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Motivated by the prompt learning in NLP, in this paper we propose to learn an image-level visual domain prompt for target domains while having the source model parameters frozen.
• Score: 14.473807945791132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-level visual domain prompt for target domains while having the source model parameters frozen. During testing, the changing target datasets can be adapted to the source model by reformulating the input data with the learned visual prompts. Specifically, we devise two types of prompts, i.e., domains-specific prompts and domains-agnostic prompts, to extract current domain knowledge and maintain the domain-shared knowledge in the continual adaptation. Furthermore, we design a homeostasis-based prompt adaptation strategy to suppress domain-sensitive parameters in domain-invariant prompts to learn domain-shared knowledge more effectively. This transition from the model-dependent paradigm to the model-free one enables us to bypass the catastrophic forgetting and error accumulation problems. Experiments show that our proposed method achieves significant performance gains over state-of-the-art methods on four widely-used benchmarks, including CIFAR-10C, CIFAR-100C, ImageNet-C, and VLCS datasets.

Related papers

• PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning [34.786268652516355]
  Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains. It seeks to align the feature representations of the source domain (where labeled data is available) and the target domain (where only unlabeled data is present)
  arXiv  Detail & Related papers  (2024-07-24T08:53:29Z)
• Adapting to Distribution Shift by Visual Domain Prompt Generation [34.19066857066073]
  We adapt a model at test-time using a few unlabeled data to address distribution shifts. We build a knowledge bank to learn the transferable knowledge from source domains. The proposed method outperforms previous work on 5 large-scale benchmarks including WILDS and DomainNet.
  arXiv  Detail & Related papers  (2024-05-05T02:44:04Z)
• Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation [84.82153655786183]
  We propose a novel framework called Informative Data Mining (IDM) to enable efficient one-shot domain adaptation for semantic segmentation. IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7%/55.4% on the GTA5/SYNTHIA to Cityscapes adaptation tasks.
  arXiv  Detail & Related papers  (2023-09-25T15:56:01Z)
• Adaptive Semantic Consistency for Cross-domain Few-shot Classification [27.176106714652327]
  Cross-domain few-shot classification (CD-FSC) aims to identify novel target classes with a few samples. We propose a simple plug-and-play Adaptive Semantic Consistency framework, which improves cross-domain robustness. The proposed ASC enables explicit transfer of source domain knowledge to prevent the model from overfitting the target domain.
  arXiv  Detail & Related papers  (2023-08-01T15:37:19Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [79.89082006155135]
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. UDA methods try to align the source and target representations to improve the generalization on the target domain. The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
  arXiv  Detail & Related papers  (2022-03-29T17:50:43Z)
• Domain Adaptation via Prompt Learning [39.97105851723885]
  Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain. We introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Prompt Learning (DAPL)
  arXiv  Detail & Related papers  (2022-02-14T13:25:46Z)
• A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation [91.13472029666312]
  We propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. Our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions.
  arXiv  Detail & Related papers  (2021-06-22T10:21:39Z)
• Source-Free Open Compound Domain Adaptation in Semantic Segmentation [99.82890571842603]
  In SF-OCDA, only the source pre-trained model and the target data are available to learn the target model. We propose the Cross-Patch Style Swap (CPSS) to diversify samples with various patch styles in the feature-level. Our method produces state-of-the-art results on the C-Driving dataset.
  arXiv  Detail & Related papers  (2021-06-07T08:38:41Z)
• Learning to Cluster under Domain Shift [20.00056591000625]
  In this work we address the problem of transferring knowledge from a source to a target domain when both source and target data have no annotations. Inspired by recent works on deep clustering, our approach leverages information from data gathered from multiple source domains. We show that our method is able to automatically discover relevant semantic information even in presence of few target samples.
  arXiv  Detail & Related papers  (2020-08-11T12:03:01Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.