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WeakCLIP: Adapting CLIP for Weakly-Supervised Semantic Segmentation

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Abstract

Contrastive language and image pre-training (CLIP) achieves great success in various computer vision tasks and also presents an opportune avenue for enhancing weakly-supervised image understanding with its large-scale pre-trained knowledge. As an effective way to reduce the reliance on pixel-level human-annotated labels, weakly-supervised semantic segmentation (WSSS) aims to refine the class activation map (CAM) and produce high-quality pseudo masks. Weakly-supervised semantic segmentation (WSSS) aims to refine the class activation map (CAM) as pseudo masks, but heavily relies on inductive biases like hand-crafted priors and digital image processing methods. For the vision-language pre-trained model, i.e. CLIP, we propose a novel text-to-pixel matching paradigm for WSSS. However, directly applying CLIP to WSSS is challenging due to three critical problems: (1) the task gap between contrastive pre-training and WSSS CAM refinement, (2) lacking text-to-pixel modeling to fully utilize the pre-trained knowledge, and (3) the insufficient details owning to the \(\frac{1}{16}\) down-sampling resolution of ViT. Thus, we propose WeakCLIP to address the problems and leverage the pre-trained knowledge from CLIP to WSSS. Specifically, we first address the task gap by proposing a pyramid adapter and learnable prompts to extract WSSS-specific representation. We then design a co-attention matching module to model text-to-pixel relationships. Finally, the pyramid adapter and text-guided decoder are introduced to gather multi-level information and integrate it with text guidance hierarchically. WeakCLIP provides an effective and parameter-efficient way to transfer CLIP knowledge to refine CAM. Extensive experiments demonstrate that WeakCLIP achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 74.0% mIoU on the val set of PASCAL VOC 2012 and 46.1% mIoU on the val set of COCO 2014. The source code and model checkpoints are released at https://github.com/hustvl/WeakCLIP.

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  1. Huazhong University of Science and Technology, Wuhan, China

    Lianghui Zhu, Xinggang Wang, Jiapei Feng, Tianheng Cheng, Yingyue Li & Bo Jiang

  2. Northwestern Polytechnical University, Xi’an, China

    Dingwen Zhang & Junwei Han

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  1. Lianghui Zhu
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Zhu, L., Wang, X., Feng, J. et al. WeakCLIP: Adapting CLIP for Weakly-Supervised Semantic Segmentation. Int J Comput Vis 133, 1085–1105 (2025). https://doi.org/10.1007/s11263-024-02224-2

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