Image Change Captioning by Learning From an Auxiliary Task

We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.