@conference {1983,
	title = {Generation and Comprehension of Unambiguous Object Descriptions},
	booktitle = {The Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2016},
	month = {06/2016},
	address = {Las Vegas, Nevada},
	abstract = {<pre>
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.</pre>

<pre>
We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene.
</pre>

<pre>
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.</pre>

<pre>
We also present a new large-scale <u>dataset</u> for referring expressions, based on</pre>

<pre>
MS-COCO.</pre>

<pre>
We have released the <u>dataset</u> and a toolbox for visualization and evaluation, see \url{<u>https</u>://<u>github</u>.com/<u>mjhucla</u>/<u>Google_Refexp_toolbox</u>}.</pre>
},
	url = {https://github.com/ mjhucla/Google_Refexp_toolbox},
	author = {Junhua Mao and Jonathan Huang and Alexander Toshev and Oana Camburu and Alan Yuille and Kevin Murphy}
}
