We explore CLIP’s dense knowledge via a novel patch-text alignment paradigm for WSSS.
- If you find this work helpful, please give us a 🌟 to receive the updation !
Feb. 26th, 2025: ExCEL is accepted by CVPR2025.- All code is released now ! 🔥🔥🔥
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP's dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP's training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO.
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tarThe augmented annotations are from SBD dataset. The download link of the augmented annotations at
DropBox. After downloading SegmentationClassAug.zip, you should unzip it and move it to VOCdevkit/VOC2012/.
VOCdevkit/
└── VOC2012
├── Annotations
├── ImageSets
├── JPEGImages
├── SegmentationClass
├── SegmentationClassAug
└── SegmentationObjectwget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zipTo generate VOC style segmentation labels for COCO, you could use the scripts provided at this repo, or just download the generated masks from Google Drive.
COCO/
├── JPEGImages
│ ├── train2014
│ └── val2014
└── SegmentationClass
├── train2014
└── val2014Please refer to the requirements.txt.
### train voc
bash run_train.sh scripts/train_voc.py [gpu_device] [gpu_number] [master_port] train_voc
### train coco
bash run_train.sh scripts/train_coco.py [gpu_devices] [gpu_numbers] [master_port] train_coco### eval voc training_free labels
bash infer_lam.sh
### eval voc seg
bash infer_seg_voc.sh tools/infer_lam.py [gpu_device] [gpu_number] [infer_set] [checkpoint_path]
### eval coco seg
bash infer_seg_coco.sh tools/infer_seg_coco.py [gpu_device] [gpu_number] [infer_set] [checkpoint_path]Semantic performance on VOC and COCO. Logs and weights are available now.
| Dataset | Backbone | Val | Test | Log |
|---|---|---|---|---|
| PASCAL VOC | ViT-B | 78.4 | 78.5 | log |
| MS COCO | ViT-B | 50.3 | - | log |
Please cite our work if you find it helpful to your reseach. 💕
@inproceedings{yang2025exploring,
title={Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation},
author={Yang, Zhiwei and Meng, Yucong and Fu, Kexue and Tang, Feilong and Wang, Shuo and Song, Zhijian},
booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
pages={20223--20232},
year={2025}
}If you have any questions, please feel free to contact the author by zwyang21@m.fudan.edu.cn.
This repo is built upon SeCo, MoRe and WeCLIP. Many thanks to their brilliant works!!!