WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels

• URL: http://arxiv.org/abs/2210.02088v1
• Date: Wed, 5 Oct 2022 08:28:57 GMT
• Title: WUDA: Unsupervised Domain Adaptation Based on Weak Source Domain Labels
• Authors: Shengjie Liu, Chuang Zhu, Wenqi Tang
• Abstract summary: Unsupervised domain adaptation (UDA) for semantic segmentation addresses the cross-domain problem with fine source domain labels. This paper defines a new task: unsupervised domain adaptation based on weak source domain labels.
• Score: 5.718326013810649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised domain adaptation (UDA) for semantic segmentation addresses the cross-domain problem with fine source domain labels. However, the acquisition of semantic labels has always been a difficult step, many scenarios only have weak labels (e.g. bounding boxes). For scenarios where weak supervision and cross-domain problems coexist, this paper defines a new task: unsupervised domain adaptation based on weak source domain labels (WUDA). To explore solutions for this task, this paper proposes two intuitive frameworks: 1) Perform weakly supervised semantic segmentation in the source domain, and then implement unsupervised domain adaptation; 2) Train an object detection model using source domain data, then detect objects in the target domain and implement weakly supervised semantic segmentation. We observe that the two frameworks behave differently when the datasets change. Therefore, we construct dataset pairs with a wide range of domain shifts and conduct extended experiments to analyze the impact of different domain shifts on the two frameworks. In addition, to measure domain shift, we apply the metric representation shift to urban landscape image segmentation for the first time. The source code and constructed datasets are available at \url{https://github.com/bupt-ai-cz/WUDA}.

Related papers

• Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis [68.742820522137]
  Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. We propose a unified bidirectional generative framework to tackle various cross-domain ABSA tasks. Our framework trains a generative model in both text-to-label and label-to-text directions.
  arXiv  Detail & Related papers  (2023-05-16T15:02:23Z)
• A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification [46.47734465505251]
  Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. We propose a two-stage framework for cross-domain text classification.
  arXiv  Detail & Related papers  (2023-04-18T06:21:40Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation [97.8552697905657]
  A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains. We propose Alleviating Semantic-level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes.
  arXiv  Detail & Related papers  (2020-04-02T03:25:05Z)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)
• Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels [78.95901454696158]
  We propose a novel Cross-Domain Self-supervised learning approach for domain adaptation. Our method significantly boosts performance of target accuracy in the new target domain with few source labels.
  arXiv  Detail & Related papers  (2020-03-18T15:11:07Z)
• Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation [105.96860932833759]
  State-of-the-art approaches prove that performing semantic-level alignment is helpful in tackling the domain shift issue. We propose to improve the semantic-level alignment with different strategies for stuff regions and for things. In addition to our proposed method, we show that our method can help ease this issue by minimizing the most similar stuff and instance features between the source and the target domains.
  arXiv  Detail & Related papers  (2020-03-18T04:43:25Z)

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.