@inproceedings{garg-etal-2024-imageinwords,
title = "{I}mage{I}n{W}ords: Unlocking Hyper-Detailed Image Descriptions",
author = "Garg, Roopal and
Burns, Andrea and
Karagol Ayan, Burcu and
Bitton, Yonatan and
Montgomery, Ceslee and
Onoe, Yasumasa and
Bunner, Andrew and
Krishna, Ranjay and
Baldridge, Jason and
Soricut, Radu",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.6",
pages = "93--127",
abstract = "Despite the longstanding adage {''}an image is worth a thousand words,{''} generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image-text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with ImageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66{\%}) and GPT-4V (+48{\%}) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31{\%} against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6{\%} on ARO, SVO-Probes, and Winoground datasets. We release the IIW-Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model.",
}
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<abstract>Despite the longstanding adage ”an image is worth a thousand words,” generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image-text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with ImageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66%) and GPT-4V (+48%) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31% against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6% on ARO, SVO-Probes, and Winoground datasets. We release the IIW-Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model.</abstract>
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%0 Conference Proceedings
%T ImageInWords: Unlocking Hyper-Detailed Image Descriptions
%A Garg, Roopal
%A Burns, Andrea
%A Karagol Ayan, Burcu
%A Bitton, Yonatan
%A Montgomery, Ceslee
%A Onoe, Yasumasa
%A Bunner, Andrew
%A Krishna, Ranjay
%A Baldridge, Jason
%A Soricut, Radu
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F garg-etal-2024-imageinwords
%X Despite the longstanding adage ”an image is worth a thousand words,” generating accurate hyper-detailed image descriptions remains unsolved. Trained on short web-scraped image-text, vision-language models often generate incomplete descriptions with visual inconsistencies. We address this via a novel data-centric approach with ImageInWords (IIW), a carefully designed human-in-the-loop framework for curating hyper-detailed image descriptions. Human evaluations on IIW data show major gains compared to recent datasets (+66%) and GPT-4V (+48%) across comprehensiveness, specificity, hallucinations, and more. We also show that fine-tuning with IIW data improves these metrics by +31% against models trained with prior work, even with only 9k samples. Lastly, we evaluate IIW models with text-to-image generation and vision-language reasoning tasks. Our generated descriptions result in the highest fidelity images, and boost compositional reasoning by up to 6% on ARO, SVO-Probes, and Winoground datasets. We release the IIW-Eval benchmark with human judgement labels, object and image-level annotations from our framework, and existing image caption datasets enriched via IIW-model.
%U https://aclanthology.org/2024.emnlp-main.6
%P 93-127
Markdown (Informal)
[ImageInWords: Unlocking Hyper-Detailed Image Descriptions](https://aclanthology.org/2024.emnlp-main.6) (Garg et al., EMNLP 2024)
ACL
- Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, and Radu Soricut. 2024. ImageInWords: Unlocking Hyper-Detailed Image Descriptions. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 93–127, Miami, Florida, USA. Association for Computational Linguistics.