Side Adapter Network for Open-Vocabulary Semantic Segmentation

CVPR 2023  ·  Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, Xiang Bai ·

This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN). Our approach models the semantic segmentation task as a region recognition problem. A side network is attached to a frozen CLIP model with two branches: one for predicting mask proposals, and the other for predicting attention bias which is applied in the CLIP model to recognize the class of masks. This decoupled design has the benefit CLIP in recognizing the class of mask proposals. Since the attached side network can reuse CLIP features, it can be very light. In addition, the entire network can be trained end-to-end, allowing the side network to be adapted to the frozen CLIP model, which makes the predicted mask proposals CLIP-aware. Our approach is fast, accurate, and only adds a few additional trainable parameters. We evaluate our approach on multiple semantic segmentation benchmarks. Our method significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed. We hope our approach will serve as a solid baseline and help ease future research in open-vocabulary semantic segmentation. The code will be available at

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Open Vocabulary Semantic Segmentation ADE20K-150 SAN mIoU 33.3 # 7
Open Vocabulary Semantic Segmentation ADE20K-847 SAN mIoU 13.7 # 7
Open Vocabulary Semantic Segmentation PASCAL Context-459 SAN mIoU 17.1 # 5
Open Vocabulary Semantic Segmentation PASCAL Context-59 SAN mIoU 60.2 # 5
Open Vocabulary Semantic Segmentation PascalVOC-20 SAN mIoU 95.5 # 4
Zero Shot Segmentation Segmentation in the Wild SAN Mean AP 41.4 # 5