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The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Generation and Comprehension of Unambiguous Object Descriptions

Open access
Mao, Junhua, Huang, Jonathan, Toshev, Alexander, Camburu, Oana, Yuille, Alan, Murphy, Kevin·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Our model is inspired by recent successes of deep learning methods for image captioning, but while image captioning is difficult to evaluate, our task allows for easy objective evaluation. We also present a new large-scale dataset for referring expressions, based on MSCOCO. We have released the dataset and a toolbox for visualization and evaluation, see https://github.com/ mjhucla/Google_Refexp_toolbox.

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Metadata

Authors
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Pages
11-20
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2963109634
Cited by
1,174
References
15
FWCI
27.72
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Generation and Comprehension of Unambiguous Object Descriptions. https://app.cochat.ai/papers/10.1109/cvpr.2016.9
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Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. (2016). Generation and Comprehension of Unambiguous Object Descriptions. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.9

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