Simultaneous Edge Alignment and Learning

Simultaneous Edge Alignment and Learning

Abstract

SEAL is a recently proposed learning framework towards edge learning under noisy labels. The framework seeks to directly generate high quality thin/crisp object semantic boundaries without any post-processing, by jointly performing edge alignment with edge learning. In particular, edge alignment is formulated as latent variable optimization and learned end-to-end during network training.

Publication
ECCV
Date