Current image captioning methods are usually trained via maximum likelihood estimation.
However, the log-likelihood score of a caption does not correlate well with human …

Abstract Evaluation metrics for image captioning face two challenges. Firstly, commonly
used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not correlate well with …

One property that remains lacking in image captions generated by contemporary methods is
discriminability: being able to tell two images apart given the caption for one of them. We …

We explore a variety of nearest neighbor baseline approaches for image captioning. These
approaches find a set of nearest neighbor images in the training set from which a caption …

Recently, the state-of-the-art models for image captioning have overtaken human
performance based on the most popular metrics, such as BLEU, METEOR, ROUGE and …

Image captioning is a challenging problem owing to the complexity in understanding the
image content and diverse ways of describing it in natural language. Recent advances in …

We present an image caption system that addresses new challenges of automatically
describing images in the wild. The challenges include generating high quality caption with …

Automatically describing an image with a natural language has been an emerging
challenge in both fields of computer vision and natural language processing. In this paper …

Two recent approaches have achieved state-of-the-art results in image captioning. The first
uses a pipelined process where a set of candidate words is generated by a convolutional …

We address personalization issues of image captioning, which have not been discussed yet
in previous research. For a query image, we aim to generate a descriptive sentence …