Papers › MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation
Jie Guo, Qimeng Wang, Yan Gao, XiaoLong Jiang, Xu Tang, Yao Hu, Baochang Zhang
CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations. In this work, we first demonstrate the necessity of image-pixel CLIP feature adaption, then provide Multi-View Prompt learning (MVP-SEG) as an effective solution to achieve image-pixel adaptation and to solve open-vocabulary semantic segmentation. Concretely, MVP-SEG deliberately learns multiple prompts trained by our Orthogonal Constraint Loss (OCLoss), by which each prompt is supervised to exploit CLIP feature on different object parts, and collaborative segmentation masks generated by all prompts promote better segmentation. Moreover, MVP-SEG introduces Global Prompt Refining (GPR) to further eliminate class-wise segmentation noise. Experiments show that the multi-view prompts learned from seen categories have strong generalization to unseen categories, and MVP-SEG+ which combines the knowledge transfer stage significantly outperforms previous methods on several benchmarks. Moreover, qualitative results justify that MVP-SEG does lead to better focus on different local parts.
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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 | MVP-SEG+ | Inductive Setting hIoU | - | #5 of 15 | Archive leaderboard | report |
| Zero-Shot Semantic Segmentation | COCO-Stuff | MVP-SEG+ | Transductive Setting hIoU | 45.5 | #5 of 15 | 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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