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We present a new self-supervised pre-training of Vision Transformers fordense prediction tasks. It is based on a contrastive loss across views thatcompares pixel-level representations to global image representations. Thisstrategy produces better local features suitable for dense prediction tasks asopposed to contrastive pre-training based on global image representation only.Furthermore, our approach does not suffer from a reduced batch size since thenumber of negative examples needed in the contrastive loss is in the order ofthe number of local features. We demonstrate the effectiveness of ourpre-training strategy on two dense prediction tasks: semantic segmentation andmonocular depth estimation.

Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks