Core Concepts
Exploring diffusion models for unsupervised landmark discovery leads to significant performance improvements.
Abstract
The content discusses the development of Pose-Guided Self-Training algorithms for Unsupervised Landmark Discovery using diffusion models. It introduces a ZeroShot baseline, D-ULD algorithm, and D-ULD++ algorithm with a focus on improving landmark detection across various datasets. The methods outperform existing state-of-the-art approaches by notable margins through self-training and clustering mechanisms.
Directory:
Abstract
Introduction
Challenges in Unsupervised Landmark Detection
Motivation for Diffusion Models
Contributions of the Study
Related Work Overview
Clustering Driven Self-Training Methods
Proposed Diffusion-Based ULD Algorithm
Proposed Zero-Shot Baseline Methodology
Proposed D-ULD Algorithm Details
Proposed D-ULD++ Algorithm Enhancements
Experiments and Results Analysis
Stats
"D-ULD++ consistently achieves remarkable performance across all datasets."
"Errors for front-facing angles are significantly lower than side-oriented ones."
"D-ULD++ outperforms Mallis (D) by notable margins."
Quotes
"Unsupervised landmarks discovery (ULD) is a challenging computer vision problem."
"Our approach consistently outperforms state-of-the-art methods on four challenging benchmarks."
"D-ULD++ brings an improvement compared to D-ULD over all pose variations."