An Object-Extensible Training Framework for Image Captioning.

Recent years have witnessed great progress in image captioning based on deep learning. However, most previous methods are limited to the original training dataset that contains only a fraction of objects in the real world. They lack the ability to describe other objects that are not in the original training dataset. In this paper, we propose an object-extensible training framework that enables a widely-used captioning paradigm to describe objects beyond the original training dataset (i.e., extended objects) by generating high-quality training data for these objects automatically. Specifically, we design a general replacement mechanism, which replaces the object (An object includes the object region in the image, and the corresponding object word in the caption) in the original training dataset with the extended object to generate new training data. The key challenge in the proposed replacement mechanism is that it should be context-aware to get the meaningful result that complies with common knowledge. We introduce the multi-modal context embedding to ensure that the generated object representation is coherent in the visual context and the generated caption is smooth and fluent in the linguistic context. Extensive experiments show that our method improves significantly over the state-of-the-art methods on the held-out MSCOCO in both automatic and human evaluation.