# Probabilistic Prompt Learning for Dense Prediction

arXiv: https://arxiv.org/abs/2304.00779 · HTML: https://mlsanity.com/paper/2304.00779/

## Key facts

- Cited by 17 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 55 of its 62 references that are in the map.
- First posted on arXiv 3 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 55)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Unified Perceptual Parsing for Scene Understanding](https://mlsanity.com/paper/1807.10221/) (2018)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding](https://mlsanity.com/paper/2104.12763/) (2021)
- [CCNet: Criss-Cross Attention for Semantic Segmentation](https://mlsanity.com/paper/1811.11721/) (2018)
- [Panoptic Feature Pyramid Networks](https://mlsanity.com/paper/1901.02446/) (2019)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [Rethinking ImageNet Pre-training](https://mlsanity.com/paper/1811.08883/) (2018)

## Built on by (most cited first, 17 of 17)

- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Prompting Language-Informed Distribution for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2305.14428/) (2023)
- [Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping](https://mlsanity.com/paper/2404.00974/) (2024)
- [Text-Region Matching for Multi-Label Image Recognition with Missing Labels](https://mlsanity.com/paper/2407.18520/) (2024)
- [MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment](https://mlsanity.com/paper/2407.21654/) (2024)
- [Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation](https://mlsanity.com/paper/2501.09138/) (2025)
- [Target Refocusing via Attention Redistribution for Open-Vocabulary Semantic Segmentation: An Explainability Perspective](https://mlsanity.com/paper/2511.16170/) (2025)
- [Faster Parameter-Efficient Tuning with Token Redundancy Reduction](https://mlsanity.com/paper/2503.20282/) (2025)
- [Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2408.05955/) (2024)
- [Probabilistic Prompt Distribution Learning for Animal Pose Estimation](https://mlsanity.com/paper/2503.16120/) (2025)
- [PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation](https://mlsanity.com/paper/2306.06656/) (2023)
- [Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2509.01275/) (2025)
- [Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation](https://mlsanity.com/paper/2405.10610/) (2024)
- [Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation](https://mlsanity.com/paper/2506.22979/) (2025)
- [Direct Segmentation without Logits Optimization for Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2604.07723/) (2026)
- [Lorentz Framework for Semantic Segmentation](https://mlsanity.com/paper/2604.16836/) (2026)
- [Learning Probabilistic Prompt for Continual Learning](https://mlsanity.com/paper/2607.04711/) (2026)

## Abstract (arXiv)

Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approach results in limited performance for dense prediction tasks that require handling more complex and diverse objects, since a single and deterministic description cannot sufficiently represent the entire image. In this paper, we present a novel probabilistic prompt learning to fully exploit the vision-language knowledge in dense prediction tasks. First, we introduce learnable class-agnostic attribute prompts to describe universal attributes across the object class. The attributes are combined with class information and visual-context knowledge to define the class-specific textual distribution. Text representations are sampled and used to guide the dense prediction task using the probabilistic pixel-text matching loss, enhancing the stability and generalization capability of the proposed method. Extensive experiments on different dense prediction tasks and ablation studies demonstrate the effectiveness of our proposed method.