Papers with Code 아카이브

Few-shot Age Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 1
보존된 평가 기록: 2

Only choose 1/2/4/8/16/32/64 samples in the training set from each class of the labels for training.

코드

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

https://github.com/kaiyangzhou/coop

공식 코드

Learning to Prompt for Vision-Language Models

https://github.com/xk-huang/OrdinalCLIP

공식 코드

OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression

https://github.com/ArsenalCheng/Meta-Adapter

Learning to Prompt for Vision-Language Models

https://github.com/Gahyeonkim09/AAPL

Learning to Prompt for Vision-Language Models

https://github.com/ThomasWangY/2024-AAAI-HPT

Learning to Prompt for Vision-Language Models

https://github.com/Vill-Lab/2024-TIP-MetaPrompt

Learning to Prompt for Vision-Language Models

https://github.com/YangYongJin/APEX

Learning to Prompt for Vision-Language Models

https://github.com/azshue/TPT

Learning to Prompt for Vision-Language Models

https://github.com/farinamatteo/zero

Learning to Prompt for Vision-Language Models

https://github.com/healthx-lab/biomedcoop

Learning to Prompt for Vision-Language Models

https://github.com/hhenryd/tap

Learning to Prompt for Vision-Language Models

https://github.com/kaiyangzhou/on-device-dg

Learning to Prompt for Vision-Language Models

https://github.com/kenomo/industrial-clip

Learning to Prompt for Vision-Language Models

https://github.com/mlvlab/dapt

Learning to Prompt for Vision-Language Models

https://github.com/muzairkhattak/multimodal-prompt-learning

Learning to Prompt for Vision-Language Models

https://github.com/muzairkhattak/protext

Learning to Prompt for Vision-Language Models

https://github.com/saic-fi/bayesian-prompt-learning

Learning to Prompt for Vision-Language Models

https://github.com/srvcodes/clap4clip

Learning to Prompt for Vision-Language Models

https://github.com/vill-lab/2024-aaai-hpt

Learning to Prompt for Vision-Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.