Cited By
View all- Liu WXu XChang HYuan XWang Z(2025)Mix-Modality Person Re-Identification: A New and Practical ParadigmACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3715142Online publication date: 28-Jan-2025
Informative Classes Matter: Towards Unsupervised Domain Adaptive Nighttime Semantic Segmentation
Pages 163 - 172
Abstract
Unsupervised Domain Adaptive Nighttime Semantic Segmentation (UDA-NSS) aims to adapt a robust model from a labeled daytime domain to an unlabeled nighttime domain. However, current advanced segmentation methods ignore the illumination effect and class discrepancies of different semantic classes during domain adaptation, showing an uneven prediction phenomenon. It is the completely ignored and underexplored issues of ''hard-to-adapt'' classes that some classes have a large performance gap between existing UDA-NSS methods and supervised learning counterparts while others have a very low performance gap. To realize ''hard-to-adapt'' classes' more sufficient learning and facilitate the UDA-NSS task, we present an Online Informative Class Sampling (OICS) strategy to adaptively mine informative classes from the target nighttime domain according to the corresponding spectrogram mean and the class frequency via our Informative Mixture of Experts. Furthermore, an Informativeness-based cross-domain Mixed Sampling (InforMS) framework is designed to focus on informative classes from the target nighttime domain by vesting their higher sampling probabilities when cross-domain mixing sampling and achieves better performance in UDA-NSS tasks. Consequently, our method outperforms state-of-the-art UDA-NSS methods by large margins on three widely-used benchmarks (e.g., ACDC, Dark Zurich, and Nighttime Driving). Notably, our method achieves state-of-the-art performance with 65.1% mIoU on ACDC-night-test and 55.4% mIoU on ACDC-night-val.
Supplemental Material
MP4 File
The video is titled "Informative Classes Matter: Towards Unsupervised Domain Adaptive Nighttime Semantic Segmentation". We focus on the completely ignored and underexplored issues of "hard-to-adapt" classes, i.e., some classes have a large performance gap between existing UDA-NSS methods and supervised learning counterparts while others have a very low performance gap. To realize "hard-to-adapt" classes' more sufficient learning and facilitate the UDA-NSS task, we present an Online Informative Class Sampling strategy to mine informative classes adaptively during domain adaptation, and an Informativeness-based cross-domain Mixed Sampling (InforMS) framework to boost the UDA-NSS task with informative classes. Experiments on three widely used benchmarks show that our proposed method outperforms state-of-the-art UDA-NSS methods by large margins and achieves a new state-of-the-art performance of nighttime semantic segmentation.
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- Informative Classes Matter: Towards Unsupervised Domain Adaptive Nighttime Semantic Segmentation
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October 2023
9913 pages
ISBN:9798400701085
DOI:10.1145/3581783
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View all- Liu WXu XChang HYuan XWang Z(2025)Mix-Modality Person Re-Identification: A New and Practical ParadigmACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3715142Online publication date: 28-Jan-2025
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