2015/01/01 by Juergen Schmidhuber · 89 citations
Computer Science · #Multimodal Machine Learning Applications #Generative Adversarial Networks and Image Synthesis #Artificial Intelligence Applications #Computer science #Artificial intelligence #Pascal (unit) #Fluency #Machine translation #Sentence #Natural language processing #Generative model #Image (mathematics) #Generative grammar #Speech recognition #Machine learning #Programming language #Linguistics
paper · pdf · doi:10.4249/scholarpedia.32832
published in Scholarpedia 10(11), 32832 (Scholarpedia Corporation)
openalex publication_date 2015/01/01 · openalex created_date 2022/05/12 · openalex updated_date 2026/07/31
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 score improvements on Flickr30k, from 55 to 66, and on SBU, from 19 to 27.