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}.
@article{arxiv.2402.14812,
title = {WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition},
author = {Lianghui Zhu and Junwei Zhou and Yan Liu and Xin Hao and Wenyu Liu and Xinggang Wang},
journal= {arXiv preprint arXiv:2402.14812},
year = {2024}
}
Comments
Accepted by ACM MM 2024. Code is available at https://github.com/hustvl/WeakSAM