Learning With Coarse Labels

Learning fine-grained representations from coarse-labeled data is a method aimed at obtaining high-precision fine-grained features by leveraging labels that are less costly to obtain but are more coarse. The goal of this task is to optimize algorithms to extract more detailed category information from coarse labels, thereby significantly reducing the cost of data annotation and improving the efficiency of model training. In the field of computer vision, this approach can be effectively applied to tasks such as image classification and object detection, enhancing the model's ability to recognize subtle differences.