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Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation

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arxiv 2305.05803 v4 pith:VXWSC4GM submitted 2023-05-09 cs.CV cs.AIcs.LG

Tianle Chen , Zheda Mai , Ruiwen Li , Wei-lun Chao This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords modelpseudo-labelsmethodssegmentationsemanticsupervisedwsssannotation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 7 Pith papers

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