Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery

arXiv:2403.16194v1 Announce Type: new
Abstract: Unsupervised landmarks discovery (ULD) for an object category is a challenging computer vision problem. In pursuit of developing a robust ULD framework, we explore the potential of a recent paradigm of self-supervised learning algorithms, known as diffusion models. Some recent works have shown that these models implicitly contain important correspondence cues. Towards harnessing the potential of diffusion models for the ULD task, we make the following core contributions. First, we propose a ZeroShot ULD baseline …

abstract algorithms arxiv clustering computer computer vision cs.cv diffusion diffusion models discovery explore framework landmark object paradigm robust self-supervised learning self-training stage supervised learning training type unsupervised vision