https://github.com/yubofeng2023/promptcl
공식 코드PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning
Papers with Code 아카이브
Prompt Learning
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuhangzang/upt
공식 코드https://github.com/yul-git/glocalclip
공식 코드GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
https://github.com/zach-zhiling-zheng/reticular_chemist
공식 코드https://github.com/zhanchuanhong/sedgpl
공식 코드What Would Happen Next? Predicting Consequences from An Event Causality Graph
https://github.com/zhangyong-tang/gmmt
공식 코드https://github.com/zhaohengz/llamp
공식 코드Large Language Models are Good Prompt Learners for Low-Shot Image Classification
https://github.com/zhengli97/promptkd
공식 코드PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
https://github.com/zhengzangw/DoPrompt
공식 코드https://github.com/zhoujiahuan1991/aaai2025-caprompt
공식 코드CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning
https://github.com/zj5559/motion-prompt-tracking
공식 코드https://github.com/zjunlp/DeepKE/tree/main/example/llm
공식 코드How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt
공식 코드Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
https://github.com/zjunlp/deepke
공식 코드How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?
https://github.com/zjunlp/promptkg
공식 코드Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
https://github.com/zjzwzw/ced-food
공식 코드Boosting Few-Shot Open-Set Object Detection via Prompt Learning and Robust Decision Boundary
https://github.com/zongqianli/prompt-tuning-survey
공식 코드https://github.com/zouxianghong/LifelongPR
공식 코드https://github.com/zqhang/anomalyclip
공식 코드AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
https://github.com/zrguo/MPLMM
공식 코드Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition
https://github.com/zybeich/ot-vp
공식 코드OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
https://github.com/ArsenalCheng/Meta-Adapter
https://github.com/MindCode-4/code-10/tree/main/MVD
https://github.com/MindSpore-scientific-2/code-10/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/MindSpore-scientific-2/code-11/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/MindSpore-scientific-2/code-2/tree/main/token_learner
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
https://github.com/PRIS-CV/PGP-SAM
PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
https://github.com/ShuvenduRoy/FER_TL_PipelineTraining
Consistency-guided Prompt Learning for Vision-Language Models
https://github.com/ThomasWangY/2024-AAAI-HPT
https://github.com/Vill-Lab/2024-TIP-MetaPrompt
https://github.com/YangYongJin/APEX
https://github.com/asif-hanif/vafa
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
https://github.com/azshue/TPT
https://github.com/bytedance/MVDream-threestudio
https://github.com/bytedance/mvdream
https://github.com/cshhzhao/awesome-cross-domain-graph-learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.