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In Computer Vision, self-supervised contrastive learning enforces similarrepresentations between different views of the same image. The pre-training ismost often performed on image classification datasets, like ImageNet, whereimages mainly contain a single class of objects. However, when dealing withcomplex scenes with multiple items, it becomes very unlikely for several viewsof the same image to represent the same object category. In this setting, wepropose SAMCLR, an add-on to SimCLR which uses SAM to segment the image intosemantic regions, then sample the two views from the same region. Preliminaryresults show empirically that when pre-training on Cityscapes and ADE20K, thenevaluating on classification on CIFAR-10, STL10 and ImageNette, SAMCLR performsat least on par with, and most often significantly outperforms not only SimCLR,but also DINO and MoCo.

SAMCLR: Contrastive pre-training on complex scenes using SAM for view sampling