While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentence captions to describe objects in context. In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating descriptions of novel objects which are not present in paired image-sentence datasets. Our method achieves this by leveraging large object recognition datasets and external text corpora and by transferring knowledge between semantically similar concepts. Current deep caption models can only describe objects contained in paired image-sentence corpora, despite the fact that they are pre-trained with large object recognition datasets, namely ImageNet. In contrast, our model can compose sentences that describe novel objects and their interactions with other objects. We demonstrate our model’s ability to describe novel concepts by empirically evaluating its performance on MSCOCO and show qualitative results on ImageNet images of objects for which no paired image-sentence data exist. Further, we extend our approach to generate descriptions of objects in video clips. Our results show that DCC has distinct advantages over existing image and video captioning approaches for generating descriptions of new objects in context.

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In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-16), pp. 1--10 2016.

@inproceedings{hendricks:cvpr16, title={Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data}, author={Lisa Anne Hendricks and Subhashini Venugopalan and Marcus Rohrbach and Raymond Mooney and Kate Saenko and Trevor Darrell}, booktitle={Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-16)}, pages={1--10}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127563", year={2016} }