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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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Abstract

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods.

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Data Availability

The datasets adopted in this study are available from the PASCAL VOC 2012 (http://host.robots.ox.ac.uk/pascal/VOC/voc2012) and the MS COCO 2014 (https://cocodataset.org).

Notes

  1. https://github.com/kazuto1011/deeplab-pytorch.

  2. https://github.com/CVI-SZU/CLIMS/tree/master/segmentation.

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Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2024YFF0618403), National Natural Science Foundation of China under Grant 82261138629, Guangdong-Macao Science and Technology Innovation Joint Fundation under Grant 2024A0505090003, Guangdong Provincial Key Laboratory under Grant 2023B1212060076, and Shenzhen Municipal Science and Technology Innovation Council under Grant JCYJ20220531101412030.

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  1. Jinheng Xie and Songhe Deng contributed equally to this work.

Authors and Affiliations

  1. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Jinheng Xie, Songhe Deng, Zhaochuan Luo & Linlin Shen

  2. Department of Computer Science, Wenzhou-Kean University, Wenzhou, 325060, China

    Linlin Shen

  3. Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

    Jinheng Xie, Songhe Deng, Zhaochuan Luo & Linlin Shen

  4. Show Lab, National University of Singapore, Singapore, 639798, Singapore

    Jinheng Xie & Mike Zheng Shou

  5. School of AI and Advanced Computing, Xi’an Jiaotong-Liverpool University, Suzhou, 215400, China

    Xianxu Hou

  6. Jarvis Research Center, Tencent YouTu Lab, Shenzhen, 518703, China

    Yawen Huang & Yefeng Zheng

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  1. Jinheng Xie
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Xie, J., Deng, S., Hou, X. et al. CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation. Int J Comput Vis 133, 5569–5588 (2025). https://doi.org/10.1007/s11263-025-02442-2

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