Fully Self-Supervised Learning for Semantic Segmentation

Understand

In this work, we present a fully self-supervised framework for semantic segmentation(FS^4).

• A fully bootstrapped strategy for semantic segmentation, which saves efforts for the huge amount of annotation, is crucial for building customized models from end-to-end for open-world domains.
• This application is eagerly needed in realistic scenarios.
• Even though recent self-supervised semantic segmentation methods have gained great progress, these works however heavily depend on the fully-supervised pretrained model and make it impossible a fully self-supervised pipeline.