Papers › Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
Cong Han, Yujie Zhong, Dengjie Li, Kai Han, Lin Ma
Recently, the open-vocabulary semantic segmentation problem has attracted increasing attention and the best performing methods are based on two-stream networks: one stream for proposal mask generation and the other for segment classification using a pretrained visual-language model. However, existing two-stream methods require passing a great number of (up to a hundred) image crops into the visual-language model, which is highly inefficient. To address the problem, we propose a network that only needs a single pass through the visual-language model for each input image. Specifically, we first propose a novel network adaptation approach, termed patch severance, to restrict the harmful interference between the patch embeddings in the pre-trained visual encoder. We then propose classification anchor learning to encourage the network to spatially focus on more discriminative features for classification. Extensive experiments demonstrate that the proposed method achieves outstanding performance, surpassing state-of-the-art methods while being 4 to 7 times faster at inference. Code: https://github.com/CongHan0808/DeOP.git
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Semantic Segmentation | COCO-Stuff | DeOP | Inductive Setting hIoU | 38.2 | #13 of 15 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | COCO-Stuff | DeOP | Transductive Setting hIoU | - | #13 of 15 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | PASCAL VOC | DeOp | Inductive Setting hIoU | 80.8 | #12 of 13 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | PASCAL VOC | DeOp | Transductive Setting hIoU | - | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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