Show and tell: A neural image caption generator
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 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.
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 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.
Research Areas
