@conference {1982,
	title = {Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images},
	booktitle = {International Conference of Computer Vision},
	year = {2015},
	month = {12/2015},
	address = {Santiago, Chile},
	abstract = {<pre>
In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions.
</pre>

<pre>
Using linguistic context and visual features, our method is able to efficiently hypothesize the semantic meaning of new words and add them to its word dictionary so that they can be used to describe images which contain these novel concepts.</pre>

<pre>
Our method has an image captioning module based on m-RNN with several improvements.</pre>

<pre>
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.</pre>

<pre>
We propose methods to prevent <u>overfitting</u> the new concepts. </pre>

<pre>
In addition, three novel concept <u>datasets</u> are constructed for this new task, and are publicly available on the project page.</pre>

<pre>
In the experiments, we show that our method effectively learns novel visual concepts from a few examples without disturbing the previously learned concepts.</pre>

<pre>
The project page is: \url{<u>www</u>.stat.<u>ucla</u>.<u>edu</u>/~<u>junhua</u>.<u>mao</u>/projects/<u>child_learning</u>.<u>html</u>}.</pre>
},
	url = {www.stat.ucla.edu/~junhua.mao/projects/child_learning.html},
	author = {Junhua Mao and Wei Xu and Yi Yang and Jiang Wang and Zhiheng Huang and Alan Yuille}
}
