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 Date: 2025-03-05 02:09:15 CET
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     Item: 
      Title: Closing the gap in domain adaptation for semantic segmentation: a time-aware method
      Children: 
       Item: 
        Title: Abstract
       Item: 
        Title: 1 Introduction
        Children: 
         Item: 
          Title: 1.1 Motivation
         Item: 
          Title: 1.2 Our proposal
         Item: 
          Title: 1.3 Contributions
       Item: 
        Title: 2 Related work
        Children: 
         Item: 
          Title: 2.1 Unsupervised domain adaptation
         Item: 
          Title: 2.2 Active domain adaptation
         Item: 
          Title: 2.3 Vision foundation models
       Item: 
        Title: 3 Method
        Children: 
         Item: 
          Title: 3.1 Annotable regions
         Item: 
          Title: 3.2 Annotation budget
         Item: 
          Title: 3.3 RADA: region-based active domain adaptation
         Item: 
          Title: 3.4 Implementation details
         Item: 
          Title: 3.5 Combining UDA with ADA
       Item: 
        Title: 4 Experiments
        Children: 
         Item: 
          Title: 4.1 Datasets
         Item: 
          Title: 4.2 Experimental settings
         Item: 
          Title: 4.3 Results
          Children: 
           Item: 
            Title: 4.3.1 Comparison with state of the art
           Item: 
            Title: 4.3.2 Further improvements
           Item: 
            Title: 4.3.3 UDA + ADA
           Item: 
            Title: 4.3.4 A new target
           Item: 
            Title: 4.3.5 Which is better, RADA or UDA + ADA ?
           Item: 
            Title: 4.3.6 SAM regions versus SLIC superpixels
           Item: 
            Title: 4.3.7 Qualitative results
       Item: 
        Title: 4.4 Limitations and errors
       Item: 
        Title: 5 Conclusions
       Item: 
        Title: Appendix A Detailed results
       Item: 
        Title: Appendix B Analysis of variance
       Item: 
        Title: Appendix C Comparison with semi-supervised methods
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        Title: Appendix D Clustering versus k-nearest neighbors for region label propagation
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        Title: References
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