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arXiv · 2410.17251

Altogether: Image Captioning via Re-aligning Alt-text

Hu Xu·Po-Yao Huang·Xiaoqing Ellen Tan·Ching-Feng Yeh·Jacob Kahn·Christine Jou·Gargi Ghosh·Omer Levy·Luke Zettlemoyer·Wen-tau Yih·Shang-Wen Li·Saining Xie·Christoph Feichtenhofer
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Abstract

This paper focuses on creating synthetic data to improve the quality of image captions. Existing works typically have two shortcomings. First, they caption images from scratch, ignoring existing alt-text metadata, and second, lack transparency if the captioners' training data (e.g. GPT) is unknown. In this paper, we study a principled approach Altogether based on the key idea to edit and re-align existing alt-texts associated with the images. To generate training data, we perform human annotation where annotators start with the existing alt-text and re-align it to the image content in multiple rounds, consequently constructing captions with rich visual concepts. This differs from prior work that carries out human annotation as a one-time description task solely based on images and annotator knowledge. We train a captioner on this data that generalizes the process of re-aligning alt-texts at scale. Our results show our Altogether approach leads to richer image captions that also improve text-to-image generation and zero-shot image classification tasks.

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Hu Xu, Po-Yao Huang, Xiaoqing Ellen Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer. 2024-12-28. Altogether: Image Captioning via Re-aligning Alt-text. https://arxiv.org/abs/2410.17251

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