S. Hwang

Learning from Adversity: Semantic-Aware Mask Refinement through Adversarial Perturbation

Phoenix is introduced, a novel framework that leverages adversarial learning to generate semantically meaningful noise patterns and contrastive learning to model refinement relationships that significantly outperforms existing methods across diverse tasks, while consistently enhancing state-of-the-art segmentation models with substantial improvements.