Papers with Code 아카이브

Multi-label zero-shot learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 0
평가표: 3
보존된 평가 기록: 19

The goal of multi-label classification task is to predict a set of labels in an image. As an extension of zero-shot learning (ZSL), multi-label zero-shot learning (ML-ZSL) is developed to identify multiple seen and unseen labels in an image.

코드

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

https://github.com/Alibaba-MIIL/ZS_SDL

공식 코드

Semantic Diversity Learning for Zero-Shot Multi-label Classification

https://github.com/akshitac8/Generative_MLZSL

공식 코드

Generative Multi-Label Zero-Shot Learning

https://github.com/akshitac8/biam

공식 코드

Discriminative Region-based Multi-Label Zero-Shot Learning

https://github.com/alibaba-miil/ml_decoder

공식 코드

ML-Decoder: Scalable and Versatile Classification Head

https://github.com/clip-italian/clip-italian

공식 코드

Contrastive Language-Image Pre-training for the Italian Language

https://github.com/fallingnight/pspg

공식 코드

Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification

https://github.com/kunimi00/ZSL_music_tagging

공식 코드

Zero-shot Learning for Audio-based Music Classification and Tagging

https://github.com/sunanhe/mkt

공식 코드

Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer

https://github.com/vladan-stojnic/zlap

공식 코드

Label Propagation for Zero-shot Classification with Vision-Language Models

https://github.com/hbdat/cvpr20_LESA

https://github.com/inars/developing_mc_for_zsl

Label-Embedding for Image Classification

https://github.com/mvp18/Popular-ZSL-Algorithms

Label-Embedding for Image Classification

https://github.com/ruotianluo/zsl-gcn-pth

Zero-Shot Learning by Convex Combination of Semantic Embeddings

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