Unsupervised landmark discovery via self-training correspondence - Mallis, Dimitrios (2023)
Object parts, also known as landmarks, convey information about an object’s shape and spatial configuration in 3D space, especially for deformable objects. The goal of landmark detection is to have a model that, for a particular object instance, can estimate the locations of its parts. Research in this field is mainly driven by supervised approaches, where a sufficient amount of human-annotated data is available. As annotating landmarks for all objects is impractical, this thesis focuses on learning landmark detectors without supervision. Despite good performance on limited scenarios (objects showcasing minor rigid deformation), unsupervised landmark discovery mostly remains an open problem. Existing work fails to capture semantic landmarks, i.e. points similar to the ones assigned by human annotators and may not generalise well to highly articulated objects like the human body, complicated backgrounds or large viewpoint variations.