Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
Authors: Bingfeng Zhang, Siyue Yu, Yunchao Wei, Yao Zhao, Jimin Xiao

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 a CLIP-based single-stage pipeline for weakly supervised semantic segmentation. Specifically the frozen CLIP model is applied as the backbone for semantic feature extraction and a new 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 cannot be optimized during training. We then propose a refinement module (RFM) to rectify them dynamically. Our architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance. Extensive experiments show that our approach significantly outperforms other approaches with less training cost. Additionally our WeCLIP also obtains promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP.