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DeCap

ICLR 2023 DeCap: Decoding CLIP Latents for Zero-shot Captioning

Install / Use

/learn @dhg-wei/DeCap
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

Official implementation for DeCap

DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training

Published at ICLR 2023

Paper link: DeCap

Data

Download coco_train to data. Download cc3m_train to data.

Training

./train_coco.sh

or

./train_cc3m.sh

Inferece

See inference_decap.ipynb.

Pretrained models

Train on coco captions: model_coco

Train on CC3M: Soon

Citation

@inproceedings{lidecap,
  title={DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training},
  author={Li, Wei and Zhu, Linchao and Wen, Longyin and Yang, Yi},
  booktitle={The Eleventh International Conference on Learning Representations}
}

Acknowledgments

This repository is heavily based on ClipCap. For training we used the data of COCO dataset and Conceptual Captions.

View on GitHub
GitHub Stars139
CategoryDevelopment
Updated14d ago
Forks12

Languages

Jupyter Notebook

Security Score

80/100

Audited on Mar 18, 2026

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