Domain-Unified Prompt Representations for Source-Free Domain Generalization

• URL: http://arxiv.org/abs/2209.14926v1
• Date: Thu, 29 Sep 2022 16:44:09 GMT
• Title: Domain-Unified Prompt Representations for Source-Free Domain Generalization
• Authors: Hongjing Niu, Hanting Li, Feng Zhao, and Bin Li
• Abstract summary: Domain generalization is a surefire way toward general artificial intelligence. It is difficult for existing methods to scale to diverse domains in open-world scenarios. We propose an approach based on large-scale vision-language pretraining models.
• Score: 6.614361539661422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization (DG), aiming to make models work on unseen domains, is a surefire way toward general artificial intelligence. Limited by the scale and diversity of current DG datasets, it is difficult for existing methods to scale to diverse domains in open-world scenarios (e.g., science fiction and pixelate style). Therefore, the source-free domain generalization (SFDG) task is necessary and challenging. To address this issue, we propose an approach based on large-scale vision-language pretraining models (e.g., CLIP), which exploits the extensive domain information embedded in it. The proposed scheme generates diverse prompts from a domain bank that contains many more diverse domains than existing DG datasets. Furthermore, our method yields domain-unified representations from these prompts, thus being able to cope with samples from open-world domains. Extensive experiments on mainstream DG datasets, namely PACS, VLCS, OfficeHome, and DomainNet, show that the proposed method achieves competitive performance compared to state-of-the-art (SOTA) DG methods that require source domain data for training. Besides, we collect a small datasets consists of two domains to evaluate the open-world domain generalization ability of the proposed method. The source code and the dataset will be made publicly available at https://github.com/muse1998/Source-Free-Domain-Generalization

Related papers

• DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization [27.099706316752254]
  We establish a novel dataset DomainVerse for Adaptive Domain Generalization (ADG) Benefiting from the introduced hierarchical definition of domain shifts, DomainVerse consists of about 0.5 million images from 390 fine-grained realistic domains. We propose two methods called Domain CLIP and Domain++ CLIP for tuning-free adaptive domain generalization.
  arXiv  Detail & Related papers  (2024-03-05T07:10:25Z)
• Domain Generalization for Domain-Linked Classes [8.738092015092207]
  In the real-world, classes may often be domain-linked, i.e. expressed only in a specific domain. We propose a Fair and cONtrastive feature-space regularization algorithm for Domain-linked DG, FOND.
  arXiv  Detail & Related papers  (2023-06-01T16:39:50Z)
• 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)
• 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)
• Reappraising Domain Generalization in Neural Networks [8.06370138649329]
  Domain generalization (DG) of machine learning algorithms is defined as their ability to learn a domain agnostic hypothesis from multiple training distributions. We find that a straightforward Empirical Risk Minimization (ERM) baseline consistently outperforms existing DG methods. We propose a classwise-DG formulation, where for each class, we randomly select one of the domains and keep it aside for testing.
  arXiv  Detail & Related papers  (2021-10-15T10:06:40Z)
• 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)
• Open Domain Generalization with Domain-Augmented Meta-Learning [83.59952915761141]
  We study a novel and practical problem of Open Domain Generalization (OpenDG) We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.
  arXiv  Detail & Related papers  (2021-04-08T09:12:24Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• 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)
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation [65.38975706997088]
  Open set domain adaptation (OSDA) assumes the presence of unknown classes in the target domain. We show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps. We propose a novel framework to specifically address the larger domain gaps.
  arXiv  Detail & Related papers  (2020-03-08T14:20:24Z)

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.