Unsupervised Semantic Segmentation Facilitates Model Understanding 事件

Unsupervised Semantic Segmentation Facilitates Model Understanding arXiv:2605.29691v1 Announce Type: new Abstract: Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of downstream tasks. Towards a better understanding of these models, a body of work has assessed the mechanics of their self-attention as well as the types of information captured across their representations, revealing, for example, st