Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to train object detectors using only image-level labels, significantly reducing annotation costs compared to fully supervised methods. Current research focuses on improving localization accuracy by leveraging transformer architectures, exploring contrastive learning strategies, and developing novel loss functions to address issues like partial activation and background noise. These advancements are crucial for expanding the applicability of deep learning to scenarios with limited labeled data, impacting fields like medical image analysis and remote sensing where obtaining precise annotations is challenging.