mlsanityData to 14 Oct 2026

Show and Tell: A Neural Image Caption Generator

arXiv 1411.4555 ↗First posted 17 Nov 2014cs.CVcited by 879

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Cited by 111 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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 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.

Key facts. Cited by 879 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Nov 2014.

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Citing papers per year: 2018: 3; 2019: 2; 2020: 1; 2021: 197; 2022: 177; 2023: 168; 2024: 137; 2025: 124; 2026: 70.

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