Cited By
View all- Xu JCao RLuo PMu D(2025)Break Adhesion: Triple adaptive-parsing for weakly supervised instance segmentationNeural Networks10.1016/j.neunet.2025.107215186(107215)Online publication date: Jun-2025
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
Pages 7947 - 7956
Abstract
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 shortcomings of requiring human prompts and category unawareness in 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.
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Index Terms
- WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
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October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
- Jianfei Cai,
- Mohan Kankanhalli,
- Balakrishnan Prabhakaran,
- Susanne Boll,
- Program Chairs:
- Ramanathan Subramanian,
- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
- Lexing Xie,
- Dong Xu
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View all- Xu JCao RLuo PMu D(2025)Break Adhesion: Triple adaptive-parsing for weakly supervised instance segmentationNeural Networks10.1016/j.neunet.2025.107215186(107215)Online publication date: Jun-2025
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