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Label-efficient and reliable semantic segmentation is essential for manyreal-life applications, especially for industrial settings with high visualdiversity, such as waste sorting. In industrial waste sorting, one of thebiggest challenges is the extreme diversity of the input stream depending onfactors like the location of the sorting facility, the equipment available inthe facility, and the time of year, all of which significantly impact thecomposition and visual appearance of the waste stream. These changes in thedata are called ``visual domains'', and label-efficient adaptation of models tosuch domains is needed for successful semantic segmentation of industrialwaste. To test the abilities of computer vision models on this task, we presentthe VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting. Ourchallenge incorporates a fully-annotated waste sorting dataset, ZeroWaste,collected from two real material recovery facilities in different locations andseasons, as well as a novel procedurally generated synthetic waste sortingdataset, 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 adaptto changing data distributions? The results of the competition show thatindustrial waste detection poses a real domain adaptation problem, that domaingeneralization techniques such as augmentations, ensembling, etc., improve theoverall performance on the unlabeled target domain examples, and thatleveraging synthetic data effectively remains an open problem. Seehttps://ai.bu.edu/visda-2022/

VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting