Frozen CLIP-DINO: A Strong Backbone for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model, while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper, we propose WeCLIP and its advanced version WeCLIP+, to build the single-stage pipeline for weakly supervised semantic segmentation. For WeCLIP, the frozen CLIP model is applied as the backbone for semantic feature extraction, and a new light decoder is designed to interpret extracted semantic features for final prediction. Meanwhile, we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels are fixed during training. We then propose a refinement module (RFM) to optimize them dynamically. For WeCLIP+, we introduce the frozen DINO model to achieve more comprehensive semantic feature extraction. The frozen DINO is combined with the frozen CLIP as the backbone, followed by a shared decoder to make predictions with less training cost. Moreover, a strengthened refinement module (RFM+) is designed to revise online pseudo labels with extra guidance from DINO features. Extensive experiments show that both WeCLIP and WeCLIP+ significantly outperform other approaches with less training cost. Particularly, WeCLIP+ gets mIoU of 83.9% on VOC 2012 test set and 56.3% on COCO val set. Additionally, these two approaches also obtain promising results for fully supervised settings.