@inproceedings{
chen2024segment,
title={Segment Anything Model ({SAM}) Enhances Pseudo-Labels for Weakly Supervised Semantic Segmentation},
author={Tianle Chen and Zheda Mai and Ruiwen Li and Wei-Lun Chao},
booktitle={I Can't Believe It's Not Better Workshop: Failure Modes in the Age of Foundation Models},
year={2024},
url={https://openreview.net/forum?id=EooD8NMyQM}
}
Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation. Most existing methods rely on Class Activation Maps (CAM) to derive pixel-level pseudo-labels and use them to train a fully supervised semantic segmentation model. Although these pseudo-labels are class-aware, indicating the coarse regions for particular classes, they are not object-aware and fail to delineate accurate object boundaries. To address this, we introduce a simple yet effective method harnessing the Segment Anything Model (SAM), a class-agnostic foundation model capable of producing fine-grained instance masks of objects, parts, and subparts. We use CAM pseudo-labels as cues to select and combine SAM masks, resulting in high-quality pseudo-labels that are both class-aware and object-aware. Our approach is highly versatile and can be easily integrated into existing WSSS methods without any modification. Despite its simplicity, our approach shows consistent gain over the state-of-the-art WSSS methods on both PASCAL VOC and MS-COCO datasets.
Segment Anything Model (SAM) Enhances Pseudo-Labels for Weakly Supervised Semantic Segmentation
Tianle Chen Zheda Mai Ruiwen Li Wei-Lun Chao