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--- |
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license: other |
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license_name: adobe-research-license |
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license_link: LICENSE |
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language: |
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- en |
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--- |
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# [ICML 2025] Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage |
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This dataset is associated with the evaluation in our ICML 2025 paper, [Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage](https://arxiv.org/abs/2412.15484). |
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## Prerequisites |
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### Packages |
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* openai>=1.14.1 |
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* python-dotenv==1.0.1 |
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### Dataset download |
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```dataset download |
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from huggingface_hub import hf_hub_download |
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local_path = hf_hub_download( |
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repo_id="saehyungl/CapMAS", |
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filename="images_capmas.tar.gz", |
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repo_type="dataset" |
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) |
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print("Downloaded to:", local_path) |
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``` |
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Or you can download it using [this URL](https://huggingface.co/datasets/saehyungl/CapMAS/resolve/main/images_capmas.tar.gz?download=true). |
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Our evaluation uses a subset of the [DOCCI](https://google.github.io/docci/) images. |
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## Captioning |
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Please generate captions for the 1,000 downloaded images for captioning evaluation. |
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Summarize the generated captions into a dictionary where the key is the corresponding image file name, and save it as a .json file. |
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```captions file |
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{ |
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"aar_test_04600.jpg": <caption_aar_test_04600>, |
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"aar_test_04601.jpg": <caption_aar_test_04601>, |
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... |
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"test_00599.json": <caption_test_00599>, |
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} |
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``` |
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You may refer to the [sample captions](https://github.com/adobe-research/CapMAS/blob/master/sample_captions/llava1.6-vicuna_llama3_th1.0/captions_final.json) for guidance. |
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## Evaluation |
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Please visit our [GitHub repository](https://github.com/adobe-research/CapMAS). |
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We provide the evaluation codes for the three metrics used in our paper: **Factuality**, **Coverage**, and **CLAIR** (Chan et al., EMNLP 2023). These evaluations rely on GPT-4o, so please fill in your OpenAI API key **OR** Azure OpenAI credentials in the `conf/gpt4o` file. |
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### Factuality (ours) |
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```factuality |
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python eval_factuality.py --image-dir <the image directory path> --captions-file <the caption .json file path> |
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``` |
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### Coverage (ours) |
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```coverage |
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python eval_coverage.py --vqa-dir data/COVERAGE_TEST_VQA --captions-file <the caption .json file path> |
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``` |
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### CLAIR |
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```clair |
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python eval_clair.py --captions-file <the caption .json file path> |
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``` |
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## References |
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1. [DOCCI (Onoe et al., ECCV 2024)](https://google.github.io/docci/#downloads) |
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2. [ImageInWords (Garg et al., EMNLP 2024)](https://github.com/google/imageinwords) |
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3. [CLAIR (Chan et al., EMNLP 2023)](https://github.com/davidmchan/clair) |
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## Cite |
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If you use the **CapMAS** dataset, filtering pipeline, or code from this repository, please cite the [paper](https://arxiv.org/pdf/2412.15484): |
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```bibtex |
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@article{lee2024toward, |
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title={Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage}, |
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author={Lee, Saehyung and Yoon, Seunghyun and Bui, Trung and Shi, Jing and Yoon, Sungroh}, |
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journal={arXiv e-prints}, |
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pages={arXiv--2412}, |
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year={2024} |
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} |
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``` |
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## License |
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The evaluation code and needle set data is licensed under the Adobe Research License. The license prohibits commercial use and allows non-commercial research use. |
