3 accepted papers
Visual generation is dominated by three paradigms: AutoRegressive (AR), diffusion, and Visual AutoRegressive (VAR) models. Unlike AR and diffusion, VARs operate on heterogeneous input structures across their generation steps, which creates severe asynchronous policy conflicts. This issue becomes par
This paper presents Uncertainty-aware Contrastive Learning (UCoL): a fully unsupervised framework for discriminative facial representation learning. Our UCoL is built upon a momentum contrastive network, referred to as Dual-path Momentum Network. Specifically, two flows of pairwise contrastive train…
Unlike most existing works that define room layout on a 2D image, we model the layout in 3D as a configuration of the camera and the room. Our spatial geometric representation with only seven variables is more concise but effective, and more importantly enables direct 3D reasoning, e.g. how the came…