https://github.com/amazon-science/vigor

https://github.com/amazon-science/vigor

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
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    Links to: arxiv.org
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    Low similarity (6.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: amazon-science
  • License: other
  • Default Branch: main
  • Size: 1.9 MB
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Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling (ECCV 2024)

This repository contains the evaluation dataset of image descriptions as described in the paper. This dataset contains paragraph-length descriptions of MSCOCO images, along with human annotated judgment of each description's correctness relative to the corresponding image and its creativity. The dataset is provided in the standard JSON format.

Attributions

The underlying images were selected from MS-COCO. The model used to generate the descriptions is LLaVA. The underlying LLM is based on LLaMA v1 by Meta (see the applicable license agreement).

Security

See CONTRIBUTING for more information.

License

This dataset is licensed under the CC-BY-NC-4.0 License. See the LICENSE file.

Citation: Bibtex

@inproceedings{yan-eccv2024-vigor, title = "ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling", author = "Yan, Siming and Bai, Min and Chen, Weifeng and Zhou, Xiong and Huang, Qixing and Li, Erran", booktitle = "Proceedings of European Conference on Computer Vision 2024", year = "2024", url = "https://arxiv.org/abs/2402.06118", }

Owner

  • Name: Amazon Science
  • Login: amazon-science
  • Kind: organization

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