Universal Domain Adaptation for Remote Sensing Image Scene Classification

• URL: http://arxiv.org/abs/2301.11387v1
• Date: Thu, 26 Jan 2023 20:04:24 GMT
• Title: Universal Domain Adaptation for Remote Sensing Image Scene Classification
• Authors: Qingsong Xu, Yilei Shi, Xin Yuan, Xiao Xiang Zhu
• Abstract summary: We propose a practical universal domain adaptation setting for remote sensing image scene classification. A novel universal domain adaptation method without source data is proposed for cases when the source data is unavailable. Empirical results show that the proposed model is effective and practical for remote sensing image scene classification.
• Score: 27.422845844752338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are often not accessible due to privacy or confidentiality issues. To this end, we propose a practical universal domain adaptation setting for remote sensing image scene classification that requires no prior knowledge on the label sets. Furthermore, a novel universal domain adaptation method without source data is proposed for cases when the source data is unavailable. The architecture of the model is divided into two parts: the source data generation stage and the model adaptation stage. The first stage estimates the conditional distribution of source data from the pre-trained model using the knowledge of class-separability in the source domain and then synthesizes the source data. With this synthetic source data in hand, it becomes a universal DA task to classify a target sample correctly if it belongs to any category in the source label set, or mark it as ``unknown" otherwise. In the second stage, a novel transferable weight that distinguishes the shared and private label sets in each domain promotes the adaptation in the automatically discovered shared label set and recognizes the ``unknown'' samples successfully. Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data is available or not. The code is available at https://github.com/zhu-xlab/UniDA.

Related papers

• Adapt Anything: Tailor Any Image Classifiers across Domains And Categories Using Text-to-Image Diffusion Models [82.95591765009105]
  We aim to study if a modern text-to-image diffusion model can tailor any task-adaptive image classifier across domains and categories. We utilize only one off-the-shelf text-to-image model to synthesize images with category labels derived from the corresponding text prompts.
  arXiv  Detail & Related papers  (2023-10-25T11:58:14Z)
• SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory [94.11898696478683]
  Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. We propose and investigate a more practical and challenging domain adaptive object detection problem under both source-free and few-shot conditions, named as SF-FSDA.
  arXiv  Detail & Related papers  (2023-06-07T12:34:55Z)
• 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)
• UMAD: Universal Model Adaptation under Domain and Category Shift [138.12678159620248]
  Universal Model ADaptation (UMAD) framework handles both UDA scenarios without access to source data. We develop an informative consistency score to help distinguish unknown samples from known samples. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.
  arXiv  Detail & Related papers  (2021-12-16T01:22:59Z)
• 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)
• Open-Set Hypothesis Transfer with Semantic Consistency [99.83813484934177]
  We introduce a method that focuses on the semantic consistency under transformation of target data. Our model first discovers confident predictions and performs classification with pseudo-labels. As a result, unlabeled data can be classified into discriminative classes coincided with either source classes or unknown classes.
  arXiv  Detail & Related papers  (2020-10-01T10:44:31Z)

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.