Image Representations and New Domains in Neural Image Captioning
Abstract
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.
Cite
@article{arxiv.1508.02091,
title = {Image Representations and New Domains in Neural Image Captioning},
author = {Jack Hessel and Nicolas Savva and Michael J. Wilber},
journal= {arXiv preprint arXiv:1508.02091},
year = {2015}
}
Comments
11 Pages, 5 Images, To appear at EMNLP 2015's Vision + Learning workshop