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

Google (United States)

PubMed
Indexed inarxivcrossrefpubmed

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…

Citation impact

912
total citations
FWCI
54.40
Percentile
100%
References
85
Citations per year

Authors

4

Topics & keywords

Keywords
  • Closed captioning
  • Computer science
  • Artificial intelligence
  • Task (project management)
  • Sentence
  • Image (mathematics)
  • Machine translation
  • Machine learning
UN Sustainable Development Goals
  • Quality Education
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