Weakly Supervised Object Localization

Weakly-Supervised Object Localization (WSOL) is a technique in the field of computer vision that aims to learn object localization using only image-level labels, without relying on object-level annotations (such as bounding boxes). This method significantly reduces the cost and difficulty of data preparation by minimizing the need for detailed annotations, while also enhancing the model's generalization capability. It is particularly suitable for object detection and localization tasks in large-scale image datasets. WSOL demonstrates significant potential in practical applications, including automated annotation, intelligent surveillance, and image retrieval.