Papers › Foundation Model Assisted Weakly Supervised Semantic Segmentation

Foundation Model Assisted Weakly Supervised Semantic Segmentation

6 Dec 2023arXiv:2312.03585archive 2025-07-28

Xiaobo Yang, Xiaojin Gong

This work aims to leverage pre-trained foundation models, such as contrastive language-image pre-training (CLIP) and segment anything model (SAM), to address weakly supervised semantic segmentation (WSSS) using image-level labels. To this end, we propose a coarse-to-fine framework based on CLIP and SAM for generating high-quality segmentation seeds. Specifically, we construct an image classification task and a seed segmentation task, which are jointly performed by CLIP with frozen weights and two sets of learnable task-specific prompts. A SAM-based seeding (SAMS) module is designed and applied to each task to produce either coarse or fine seed maps. Moreover, we design a multi-label contrastive loss supervised by image-level labels and a CAM activation loss supervised by the generated coarse seed map. These losses are used to learn the prompts, which are the only parts need to be learned in our framework. Once the prompts are learned, we input each image along with the learned segmentation-specific prompts into CLIP and the SAMS module to produce high-quality segmentation seeds. These seeds serve as pseudo labels to train an off-the-shelf segmentation network like other two-stage WSSS methods. Experiments show that our method achieves the state-of-the-art performance on PASCAL VOC 2012 and competitive results on MS COCO 2014. Code is available at https://github.com/HAL-42/FMA-WSSS.git.

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Tasks

Image ClassificationSegmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentationimage-classificationmodel

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Weakly-Supervised Semantic Segmentation COCO 2014 val FMA-WSSS (Swin-L) mIoU 55.4 #4 of 39 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 test FMA-WSSS (Swin-L) Mean IoU 81.6 #3 of 60 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 train FMA-WSSS Mean IoU 80.4 #1 of 4 Archive leaderboard report
Weakly-Supervised Semantic Segmentation PASCAL VOC 2012 val FMA-WSSS (Swin-L) Mean IoU 82.6 #2 of 73 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

CAMCLIPSAM

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