Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions

Ronghang Hu; Marcus Rohrbach; Subhashini Venugopalan; Trevor Darrell

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arXiv · 1608.08305

Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions

Ronghang Hu·Marcus Rohrbach·Subhashini Venugopalan·Trevor Darrell

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Abstract

Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression. One major difficulty to train such language-based image segmentation systems is the lack of datasets with joint vision and text annotations. Although existing vision datasets such as MS COCO provide image captions, there are few datasets with region-level textual annotations for images, and these are often smaller in scale. In this paper, we explore how existing large scale vision-only and text-only datasets can be utilized to train models for image segmentation from referring expressions. We propose a method to address this problem, and show in experiments that our method can help this joint vision and language modeling task with vision-only and text-only data and outperforms previous results.

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BibTeX RIS

Ronghang Hu, Marcus Rohrbach, Subhashini Venugopalan, Trevor Darrell. 2016-08-30. Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions. https://arxiv.org/abs/1608.08305

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