View Crafting For Instance-Level Representation from Scene Images

Existing image-level self-supervised learning (SSL) methods pre-trained on natural scene data can have difficulty in adating to dense prediction tasks. However, scene images contain multiple varied instances. We devise two techniques to craft high-quality scene and instance views for instance-level SSL. Firstly, we leverage the prior from the image-level pre-trained model to discover the salient instances and the correspondence between cross-image instance pairs. Then, we augment multiple instances to compose the scene views, where both scene similarity and variance are promoted. Meanwhile, we encourage the scene and inner instances to align in feature space to model the scene-instance correlation. Experiments show its SOTA performance in dense prediction tasks.