Computer Science > Computer Vision and Pattern Recognition
arXiv:2303.14828 (cs)
[Submitted on 26 Mar 2023]
Title:VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Authors:Dina Bashkirova,Samarth Mishra,Diala Lteif,Piotr Teterwak,Donghyun Kim,Fadi Alladkani,James Akl,Berk Calli,Sarah Adel Bargal,Kate Saenko,Daehan Kim,Minseok Seo,YoungJin Jeon,Dong-Geol Choi,Shahaf Ettedgui,Raja Giryes,Shady Abu-Hussein,Binhui Xie,Shuang Li
View a PDF of the paper titled VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting, by Dina Bashkirova and 18 other authors
View PDFAbstract:Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sorting. In industrial waste sorting, one of the biggest challenges is the extreme diversity of the input stream depending on factors like the location of the sorting facility, the equipment available in the facility, and the time of year, all of which significantly impact the composition and visual appearance of the waste stream. These changes in the data are called ``visual domains'', and label-efficient adaptation of models to such domains is needed for successful semantic segmentation of industrial waste. To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting. Our challenge incorporates a fully-annotated waste sorting dataset, ZeroWaste, collected from two real material recovery facilities in different locations and seasons, as well as a novel procedurally generated synthetic waste sorting dataset, SynthWaste. In this competition, we aim to answer two questions: 1) can we leverage domain adaptation techniques to minimize the domain gap? and 2) can synthetic data augmentation improve performance on this task and help adapt to changing data distributions? The results of the competition show that industrial waste detection poses a real domain adaptation problem, that domain generalization techniques such as augmentations, ensembling, etc., improve the overall performance on the unlabeled target domain examples, and that leveraging synthetic data effectively remains an open problem. Seethis https URL
| Comments: | Proceedings of Machine Learning Research |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2303.14828 [cs.CV] |
| (orarXiv:2303.14828v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2303.14828 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting, by Dina Bashkirova and 18 other authors
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