While an important problem in the vision community is to design algorithms that can
automatically caption images, few publicly-available datasets for algorithm development …

One of the ways blind people understand their surroundings is by clicking images and
relying on descriptions generated by image captioning systems. Current work on captioning …

Image captioning has recently demonstrated impressive progress largely owing to the
introduction of neural network algorithms trained on curated dataset like MS-COCO. Often …

Image captioning is an important task for benchmarking visual reasoning and for enabling
accessibility for people with vision impairments. However, as in many machine learning …

A wide range of image captioning models has been developed, achieving significant
improvement based on popular metrics, such as BLEU, CIDEr, and SPICE. However …

Automatic caption generation in natural language to describe the visual content of an image
has attracted an increasing amount of attention in the last decade due to its potential …

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 …

Current image captioning systems perform at a merely descriptive level, essentially
enumerating the objects in the scene and their relations. Humans, on the contrary, interpret …

Visual attention has shown usefulness in image captioning, with the goal of enabling a
caption model to selectively focus on regions of interest. Existing models typically rely on top …

We explore the use of a knowledge graphs, that capture general or commonsense
knowledge, to augment the information extracted from images by the state-of-the-art …