# Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition

arXiv: https://arxiv.org/abs/2304.04704 · HTML: https://mlsanity.com/paper/2304.04704/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 50 of its 71 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)

## Built on by (most cited first, 30 of 35)

- [GLaMM: Pixel Grounding Large Multimodal Model](https://mlsanity.com/paper/2311.03356/) (2023)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models](https://mlsanity.com/paper/2405.20985/) (2024)
- [PromptKD: Unsupervised Prompt Distillation for Vision-Language Models](https://mlsanity.com/paper/2403.02781/) (2024)
- [A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future](https://mlsanity.com/paper/2307.09220/) (2023)
- [Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond](https://mlsanity.com/paper/2310.02071/) (2023)
- [TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding](https://mlsanity.com/paper/2310.19060/) (2023)
- [Multi-modal Queried Object Detection in the Wild](https://mlsanity.com/paper/2305.18980/) (2023)
- [PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain](https://mlsanity.com/paper/2402.15527/) (2024)
- [AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation](https://mlsanity.com/paper/2407.04603/) (2024)
- [Link-Context Learning for Multimodal LLMs](https://mlsanity.com/paper/2308.07891/) (2023)
- [Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection](https://mlsanity.com/paper/2406.00510/) (2024)
- [Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation](https://mlsanity.com/paper/2407.03056/) (2024)
- [Simple Image-level Classification Improves Open-vocabulary Object Detection](https://mlsanity.com/paper/2312.10439/) (2023)
- [World to Code: Multi-modal Data Generation via Self-Instructed Compositional Captioning and Filtering](https://mlsanity.com/paper/2409.20424/) (2024)
- [SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection](https://mlsanity.com/paper/2405.10053/) (2024)
- [Auto-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2312.04539/) (2023)
- [OV-VG: A Benchmark for Open-Vocabulary Visual Grounding](https://mlsanity.com/paper/2310.14374/) (2023)
- [Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion](https://mlsanity.com/paper/2311.03352/) (2023)
- [Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification](https://mlsanity.com/paper/2306.02243/) (2023)
- [Kaputt: A Large-Scale Dataset for Visual Defect Detection](https://mlsanity.com/paper/2510.05903/) (2025)
- [DPL: Decoupled Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2308.10061/) (2023)
- [Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment](https://mlsanity.com/paper/2308.12960/) (2023)
- [Enhancing Fine-Grained Image Classifications via Cascaded Vision Language Models](https://mlsanity.com/paper/2405.11301/) (2024)
- [Revisiting Prompt Pretraining of Vision-Language Models](https://mlsanity.com/paper/2409.06166/) (2024)
- [Robust Adaptation of Foundation Models with Black-Box Visual Prompting](https://mlsanity.com/paper/2407.17491/) (2024)
- [Generalized Few-Shot Out-of-Distribution Detection](https://mlsanity.com/paper/2508.05732/) (2025)
- [Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models](https://mlsanity.com/paper/2508.17417/) (2025)
- [Beyond the Seen: Bounded Distribution Estimation for Open-Vocabulary Learning](https://mlsanity.com/paper/2510.04770/) (2025)
- [AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning](https://mlsanity.com/paper/2511.21188/) (2025)

## Abstract (arXiv)

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.