Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relies on multi-instance learning and pseudo-labeling. This paper introduces WeakSAM and solves the weakly-supervised object detection (WSOD) and segmentation by utilizing the pre-learned world knowledge contained in a vision foundation model, i.e., the Segment Anything Model (SAM). WeakSAM addresses two critical limitations in traditional WSOD retraining, i.e., pseudo ground truth (PGT) incompleteness and noisy PGT instances, through adaptive PGT generation and Region of Interest (RoI) drop regularization. It also addresses the SAM's problems of requiring prompts and category unawareness for automatic object detection and segmentation. Our results indicate that WeakSAM significantly surpasses previous state-of-the-art methods in WSOD and WSIS benchmarks with large margins, i.e. average improvements of 7.4% and 8.5%, respectively. The code is available at \url{https://github.com/hustvl/WeakSAM}.

翻译：利用不精确监督进行弱监督视觉识别是一个关键但具有挑战性的学习问题。它显著降低了人工标注成本，传统上依赖于多实例学习和伪标签技术。本文提出WeakSAM，通过利用视觉基础模型——即分割一切模型（SAM）中包含的预训练世界知识，解决了弱监督目标检测（WSOD）和分割问题。WeakSAM通过自适应伪真值生成和感兴趣区域丢弃正则化，解决了传统WSOD再训练中的两个关键局限性，即伪真值不完整和含噪PGT实例。该方法同时解决了SAM在自动目标检测与分割中需要提示以及类别不可知的问题。实验结果表明，WeakSAM在WSOD和WSIS基准测试中以大幅优势显著超越先前最先进方法，平均提升分别为7.4%和8.5%。代码已开源：\url{https://github.com/hustvl/WeakSAM}。