https://github.com/kakaobrain/coyo-dataset
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Papers with Code 아카이브
Zero-Shot Image Classification
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Zero-shot image classification is a technique in computer vision where a model can classify images into categories that were not present during training. This is achieved by leveraging semantic information about the categories, such as textual descriptions or relationships between classes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/laion-ai/clip_benchmark
https://github.com/lucidrains/x-clip
https://github.com/microsoft/GLIP
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/esvit
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/klite
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/microsoft/unicl
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/mlfoundations/open_clip
https://github.com/nahidalam/open_clip
Reproducible scaling laws for contrastive language-image learning
https://github.com/openrobotlab/ov_parts
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
https://github.com/pwc-1/Paper-8/tree/main/align
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/pwc-1/Paper-8/tree/main/altclip
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/shkarupa-alex/tfclip
Reproducible scaling laws for contrastive language-image learning
https://github.com/sincerass/mvlpt
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/willard-yuan/video-text-retrieval-papers
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
https://github.com/zhanghr2001/promptta
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
https://github.com/zjukg/structure-clip
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.