Statistical and Spatial Consensus Collection for Detector Adaption

This course covers the following learning outcomes and goals: understanding past and present research topics in detector adaptation, learning about domain adaptation and the proposed approach, exploring target sample selection using a RANSAC-like approach, and understanding the spatial consensus collection process for ensemble decision-making. The course teaches individual skills such as training details, collecting spatial consensus, implementing a spatially-dependent majority vote rule, determining initial classifier vocabulary cardinality, and utilizing different loss functions and RANSAC-like boosting techniques. The teaching method of the course involves presenting theoretical concepts, discussing research outcomes, and providing practical examples and results to illustrate the concepts. The intended audience for this course includes researchers, practitioners, and students interested in statistical and spatial consensus collection for detector adaptation, domain adaptation, and ensemble decision-making in the field of computer vision and machine learning.