https://github.com/darl-libsignal/promptgat
공식 코드Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt 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/endoluminalsurgicalvision-imr/pass
공식 코드PASS:Test-Time Prompting to Adapt Styles and Semantic Shapes in Medical Image Segmentation
https://github.com/engindeniz/vitis
공식 코드Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts
https://github.com/eric-ai-lab/dsd
공식 코드Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners
https://github.com/eyalbd2/PADA
공식 코드PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
https://github.com/fallingnight/pspg
공식 코드https://github.com/fcjian/PromptDet
공식 코드PromptDet: Towards Open-vocabulary Detection using Uncurated Images
https://github.com/feiliu36/promptvrp
공식 코드https://github.com/fereshteshakeri/few-shot-medvlms
공식 코드https://github.com/fereshteshakeri/fewshot-clip-strong-baseline
공식 코드https://github.com/fourierai/idea
공식 코드https://github.com/funz-0/promptad
공식 코드PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
https://github.com/g-u-n/pycil
공식 코드S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
https://github.com/ganchengguang/resume-IE-via-prompt
공식 코드A Few-shot Approach to Resume Information Extraction via Prompts
https://github.com/gaojingsheng/lamm
공식 코드https://github.com/georgeluimmortal/perplection_acl2023
공식 코드What Makes Pre-trained Language Models Better Zero-shot Learners?
https://github.com/gingl/cmpa
공식 코드https://github.com/gongchenghua/self-pro
공식 코드Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks
https://github.com/google-research/google-research
공식 코드https://github.com/haonenglin/awesome-vlm-for-mia
공식 코드Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review
https://github.com/haoranchen/multi-prompt-alignment-for-msuda
공식 코드Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
https://github.com/harderthenharder/transformers_tasks
공식 코드Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models
https://github.com/has97/osloprompt
공식 코드OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
https://github.com/healthx-lab/biomedcoop
공식 코드BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models
https://github.com/hey-it-s-me/corple
공식 코드https://github.com/hhenryd/tap
공식 코드Tree of Attributes Prompt Learning for Vision-Language Models
https://github.com/hkuds/graphpl
공식 코드GraphPro: Graph Pre-training and Prompt Learning for Recommendation
https://github.com/hkuds/graphpro
공식 코드GraphPro: Graph Pre-training and Prompt Learning for Recommendation
https://github.com/hlr/gipcol
공식 코드GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning
https://github.com/hongxialee/fedotp
공식 코드Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
https://github.com/hoqolo/sdstrack
공식 코드https://github.com/hsu-chia-ming/prompt-xre
공식 코드https://github.com/huaqlili/prompt-sid
공식 코드https://github.com/hulianyuyy/deep_correlated_prompting
공식 코드Deep Correlated Prompting for Visual Recognition with Missing Modalities
https://github.com/husteryoung/mplt
공식 코드https://github.com/hustminslab/teprompt
공식 코드TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.