  Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation
models using 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 capture only the discriminative
image regions of 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 latent space to enhance
the semantic alignment between the segmented regions and the target object
categories. More specifically, we propose Contrastive Prompt Learning and
Prompt-guided Semantic Refinement to learn the prompts that adequately describe
and suppress the co-occurring backgrounds associated with each target object
category. In this way, SemPLeS can perform better semantic alignment between
object regions and the associated class 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 shows compatibility with other WSSS methods. The source codes are
provided in the supplementary.
