Show and Tell: A Neural Image Caption Generator

TL;DR

This paper introduces a deep recurrent neural network model that generates natural language descriptions of images, demonstrating high accuracy and fluency across multiple datasets, advancing the state-of-the-art in image captioning.

Contribution

The paper presents a novel neural image captioning model combining computer vision and machine translation techniques, achieving superior performance on standard benchmarks.

Findings

Achieved BLEU-1 score of 59 on Pascal dataset, close to human performance.

Improved BLEU-1 scores on Flickr30k and SBU datasets.

Set new state-of-the-art BLEU-4 score of 27.7 on COCO dataset.

Abstract

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.