Mao, Junhua;
Huang, Jonathan;
Toshev, Alexander;
Camburu, Oana;
Yuille, Alan;
Murphy, Kevin;
(2016)
Generation and Comprehension of Unambiguous Object Descriptions.
In:
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 11-20).
IEEE: Las Vegas, NV, USA.
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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.
Type: | Proceedings paper |
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Title: | Generation and Comprehension of Unambiguous Object Descriptions |
Event: | 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Dates: | 27 Jun 2016 - 30 Jun 2016 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/cvpr.2016.9 |
Publisher version: | http://dx.doi.org/10.1109/cvpr.2016.9 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Context , Google, Visualization, Training, Machine learning, Recurrent neural networks, Automobiles |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10184044 |
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