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In this paper, we address the task of learning novel visual concepts, andtheir interactions with other concepts, from a few images with sentencedescriptions. Using linguistic context and visual features, our method is ableto efficiently hypothesize the semantic meaning of new words and add them toits word dictionary so that they can be used to describe images which containthese novel concepts. Our method has an image captioning module based on m-RNNwith several improvements. In particular, we propose a transposed weightsharing scheme, which not only improves performance on image captioning, butalso makes the model more suitable for the novel concept learning task. Wepropose methods to prevent overfitting the new concepts. In addition, threenovel concept datasets are constructed for this new task. In the experiments,we show that our method effectively learns novel visual concepts from a fewexamples without disturbing the previously learned concepts. The project pageis http://www.stat.ucla.edu/~junhua.mao/projects/child_learning.html

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images