Papers › Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alex Smola, Xu sun
This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification, semantic segmentation, and object detection, to boost recognition performances in a zero-shot manner. Empirical evaluation shows that POMP achieves state-of-the-art performances on 21 datasets, e.g., 67.0% average accuracy on 10 classification datasets (+3.1% compared to CoOp) and 84.4 hIoU on open-vocabulary Pascal VOC segmentation (+6.9 compared to ZSSeg). Our code is available at https://github.com/amazon-science/prompt-pretraining.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open Vocabulary Object Detection | LVIS v1.0 | POMP | AP novel-LVIS base training | 25.2 | #16 of 28 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | COCO-Stuff-171 | POMP | HIoU | 39.1 | #1 of 7 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PascalVOC-20 | POMP | hIoU | 84.4 | #13 of 20 | Archive leaderboard | report |
| Open Vocabulary Semantic Segmentation | PascalVOC-20 | POMP | mIoU | 89.4 | #13 of 20 | Archive leaderboard | report |
| Prompt Engineering | ImageNet-21k | POMP | Accuracy | 25.3 | #1 of 2 | Archive leaderboard | report |
| Prompt Engineering | ImageNet-A | POMP | Top-1 accuracy % | 51.6 | #1 of 9 | Archive leaderboard | report |
| Prompt Engineering | ImageNet-R | POMP | Top-1 accuracy % | 77.9 | #1 of 9 | Archive leaderboard | report |
| Prompt Engineering | ImageNet-S | POMP | Top-1 accuracy % | 49.8 | #1 of 9 | 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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