Image Representations and New Domains in Neural Image Captioning
This work addresses the understanding of neural captioning mechanisms for researchers, showing it is incremental by questioning the role of image features.
The study investigated whether recent advances in neural image captioning are primarily driven by language models rather than image representations, finding that a state-of-the-art algorithm produces quality captions even with poor image inputs, and replicated this in a new domain with 66K recipe image/title pairs.
We examine the possibility that recent promising results in automatic caption generation are due primarily to language models. By varying image representation quality produced by a convolutional neural network, we find that a state-of-the-art neural captioning algorithm is able to produce quality captions even when provided with surprisingly poor image representations. We replicate this result in a new, fine-grained, transfer learned captioning domain, consisting of 66K recipe image/title pairs. We also provide some experiments regarding the appropriateness of datasets for automatic captioning, and find that having multiple captions per image is beneficial, but not an absolute requirement.