SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
arxiv(2024)
摘要
Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation
models using training image data with only image-level supervision. Since
precise pixel-level annotations are not accessible, existing methods typically
focus on producing pseudo masks for training segmentation models by refining
CAM-like heatmaps. However, the produced heatmaps may only capture
discriminative image regions of target object categories or the associated
co-occurring backgrounds. To address the issues, we propose a Semantic Prompt
Learning for WSSS (SemPLeS) framework, which learns to effectively prompt the
CLIP space to enhance the semantic alignment between the segmented regions and
the target object categories. More specifically, we propose Contrastive Prompt
Learning and Class-associated Semantic Refinement to learn the prompts that
adequately describe and suppress the image backgrounds associated with each
target object category. In this way, our proposed framework is able to perform
better semantic matching between object regions and the associated text labels,
resulting in desired pseudo masks for training the segmentation model. The
proposed SemPLeS framework achieves SOTA performance on the standard WSSS
benchmarks, PASCAL VOC and MS COCO, and demonstrated interpretability with the
semantic visualization of our learned prompts. The codes will be released.
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