[1411.4555] Show and Tell: A Neural Image Caption Generator

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

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@allowfirm
@allowfirm かつて「Show and Tell」( t.co/fM13hjRH2p )の結果を初めて見たとき、どのように実現しているか想像できず衝撃を受けました。先日、深層学習のエッセンスは特徴抽出との主張を見かけて納得するとともに、まだ新たに発展する方向もありそうと思いました。

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  • 2021/12/31: デザインを刷新しました
  • 2021/04/08: 日本語Kaggleのカテゴリを新設しました