Connecting Vision and Language with Localized Narratives

Connecting Vision and Language with Localized Narratives

Abstract

We propose Localized Narratives, an efficient way to collect image captions with dense visual grounding. We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing. Since the voice and the mouse pointer are synchronized, we can localize every single word in the description. This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data. We annotate k images with Localized Narratives: the whole COCO dataset and k images of the Open Images dataset, which we make publicly available. We provide an extensive analysis of these annotations and demonstrate their utility on two applications which benefit from our mouse trace: controlled image captioning and image generation.

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01_Introduction \subfile02_Related \subfile03_Generation \subfile04_Analysis \subfile05_Applications \subfile05_GANs

1 Conclusions

This paper introduces Localized Narratives, an efficient way to collect image captions in which every single word is visually grounded by a mouse trace. We annotated k images with Localized Narratives: the whole COCO dataset [33] and k images of Open Images [31, 28]. Our analysis shows that our data is rich and provides accurate grounding. We demonstrated the utility of our data through two applications which use the mouse traces: controlled image captioning and image generation.

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