Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge

TLDR

Image captioning is a core AI problem linking computer vision and NLP, and a 2015 competition on the COCO dataset spurred renewed interest. The authors propose a deep recurrent generative model that fuses computer vision and machine translation techniques to produce natural image descriptions. The model is trained by maximizing likelihood of target captions, refined with baseline improvements, and evaluated on multiple datasets and the 2015 COCO competition. The approach achieves accurate, fluent captions, as confirmed qualitatively and quantitatively, and tied for first place in the 2015 COCO competition.

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. Finally, given the recent surge of interest in this task, a competition was organized in 2015 using the newly released COCO dataset. We describe and analyze the various improvements we applied to our own baseline and show the resulting performance in the competition, which we won ex-aequo with a team from Microsoft Research.