UTILIZING REPRESENTATION LEARNING TECHNIQUES FOR SOLVING APPLIED AND INDUSTRIAL PROBLEMS IN COMPUTER VISION

Gurov A. V., Kamilov E. M.

The paper investigates methods for solving applied and industrial problems in the field of computer vision, with a particular focus on granulometry. Despite advancements in machine learning, such tasks lack effective solutions due to limited annotated data. The paper reviews existing approaches, emphasizing representation learning methods, including those from related fields. The findings highlight the potential and limitations of current approaches, indicating the need for further research to effectively address such challenges.

Keywords:computer vision, segmentation, representation learning, foundational models