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Prompt Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/0-ml/flip

공식 코드

FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning

https://github.com/072jiajia/image-text-co-decomposition

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Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation

https://github.com/0xfafa/fa

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FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection

https://github.com/12138-chr/mtair

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Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation

https://github.com/1adrianb/lasp

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models

https://github.com/983632847/WebUAV-3M

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Awesome Multi-modal Object Tracking

https://github.com/983632847/awesome-multimodal-object-tracking

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Awesome Multi-modal Object Tracking

https://github.com/BIT-ENGD/codeclassprompt

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Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features

https://github.com/Bala93/CLIPCalib

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Robust Calibration of Large Vision-Language Adapters

https://github.com/CHENGY12/PLOT

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PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

https://github.com/ChaoLiang-HUST/ICCL

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In-context Contrastive Learning for Event Causality Identification

https://github.com/Cuberick-Orion/Bi-Blip4CIR

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Bi-directional Training for Composed Image Retrieval via Text Prompt Learning

https://github.com/DianaNerualNetwork/StainPromptInversion

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Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion

https://github.com/Gahyeonkim09/AAPL

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AAPL: Adding Attributes to Prompt Learning for Vision-Language Models

https://github.com/GeorgeLuImmortal/PUnifiedNER

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PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets

https://github.com/GongShuai8210/PLAN

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Federated Domain Generalization via Prompt Learning and Aggregation

https://github.com/HauschildLab/MGPATH

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MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification

https://github.com/Hrren/Beyond-Words

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Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning

https://github.com/JacksonWuxs/MeNSP

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Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education

https://github.com/KU-CVLAB/LANIT

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LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

https://github.com/KyanChen/RSPrompter

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RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

https://github.com/LeapLabTHU/DAPrompt

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Domain Adaptation via Prompt Learning

https://github.com/LimboChen/JY-QuotePlus

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Identifying Speakers and Addressees of Quotations in Novels with Prompt Learning

https://github.com/LiuShiBen/DAFC

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Distribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification

https://github.com/PJLallen/D2GPLand

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Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection

https://github.com/PKU-ICST-MIPL/FinePOSE_CVPR2024

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FinePOSE: Fine-Grained Prompt-Driven 3D Human Pose Estimation via Diffusion Models

https://github.com/PanBikang/PromptFolio

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Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method

https://github.com/RikoLi/PCL-CLIP

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Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification

https://github.com/Shuyu-XJTU/APTM

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Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark

https://github.com/XiaoBuL/SYNC-CLIP

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SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios

https://github.com/Yanyi-Zhang/CompILer

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Not Just Object, But State: Compositional Incremental Learning without Forgetting

https://github.com/YuanYuan98/UrbanDiT

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UrbanDiT: A Foundation Model for Open-World Urban Spatio-Temporal Learning

https://github.com/Zhenhan-Huang/Modular-Prompt-Learning

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Modular Prompt Learning Improves Vision-Language Models

https://github.com/ai-hpc-research-team/slm4crp

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A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions

https://github.com/aim-uofa/segprompt

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SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning

https://github.com/aimagelab/mammoth

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CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning

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