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Pixel-level labels are particularly expensive to acquire. Hence, pretrainingis a critical step to improve models on a task like semantic segmentation.However, prominent algorithms for pretraining neural networks use image-levelobjectives, e.g. image classification, image-text alignment a la CLIP, orself-supervised contrastive learning. These objectives do not model spatialinformation, which might be sub-optimal when finetuning on downstream taskswith spatial reasoning. In this work, we pretrain network with a location-aware(LOCA) self-supervised method which fosters the emergence of strong densefeatures. Specifically, we use both a patch-level clustering scheme to minedense pseudo-labels and a relative location prediction task to encouragelearning about object parts and their spatial arrangements. Our experimentsshow that LOCA pretraining leads to representations that transfer competitivelyto challenging and diverse semantic segmentation datasets.

Location-Aware Self-Supervised Transformers for Semantic Segmentation