The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
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-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.
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“They coupled a convolutional neural network for encoding of the image, with a recurrent neural network - specifically a long-short term memory net (LSTM) (Hochreiter and Schmidhuber (1997)) - for textual encoding.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
In the citing paper
“They used human-generated sentences as ground truth for training, and used the bilingual evaluation understudy (BLEU) metric for evaluation.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
In the citing paper
“Vinyals et al. (2015) provided textual explanation for images using an end-to-end image captioning framework.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
In the citing paper
“Similarly, an image and a natural language caption can capture complimentary but converging information about a scene [32, 35].”
From Multimodal Generative Models for Scalable Weakly-Supervised Learning (2018) · cites this paper as [32]
In the citing paper
“Dependence on complex datasets including images and textual annotations”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [43]
In the citing paper
“Semantic relationship between images and textual descriptions is intuitive, even for nonexperts”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [43]
Citations
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- kuznetsova, 2014, Treetalk: Composition and compression of trees for image descriptions, ACL, 2
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“…example, when pre-training a model to do image classification, the induced features transfer reasonably well to other image classification domains, but also lack certain information such as color or the ability to count that are irrelevant for classification but relevant for e.g. image captioning [4].”
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“Interestingly, the paradigm of CoCoOp is analogous to image captioning [49], which explains why instance-conditional prompts are more generalizable: they are optimized to characterize each instance (more robust to class shift) rather than to serve only for some specific classes .”
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From captions to visual concepts and back
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MVAE: Multimodal Variational Autoencoder for Fake News Detection
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Sequence-Level Knowledge Distillation
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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
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“Visual recognition tasks involving language, such as captioning (Vinyals et al., 2015), visual question answering (Antol et al.”
BackgroundNeural Network Methods for Natural Language Processing
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Flamingo: a visual language model for few-shot learning
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“[116] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan.”
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“Pioneer work in image captioning [4, 5, 13, 45, 47], sentence-based image retrieval [14, 40] and visual QA [1, 7, 36] shows promising results.”
BackgroundToward Controlled Generation of TextPreprint
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Learning Deep Representations of Fine-Grained Visual Descriptions
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A hybrid deep learning based traffic flow prediction method and its understanding
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Learning Deep Structure-Preserving Image-Text Embeddings
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Boosting Image Captioning with Attributes
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“…(Krizhevsky et al., 2012; He et al., 2016), information retrieval (Huang et al., 2013; Elkahky et al., 2015), multimodal intelligence (Fang et al., 2015; Vinyals et al., 2015; Karpathy and Fei-fei, 2015; He and Deng, 2017), and empathic conversational systems (Fung et al., 2016; Alam et al., 2017).”
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X-Linear Attention Networks for Image Captioning
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Language to Logical Form with Neural Attention
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Generating Radiology Reports via Memory-driven Transformer
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MovieQA: Understanding Stories in Movies through Question-Answering
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Metadata
- Authors
- Vinyals, O., Toshev, A., Bengio, S., & Erhan, D.
- Affiliations
- Google (United States) (US)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3156-3164
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1895577753
- Cited by
- 6,318
- References
- 40
- Citations by year
- 2026: 228 · 2025: 464 · 2024: 547 · 2023: 598 · 2022: 589 · 2021: 747 · 2020: 748 · 2019: 770 · 2018: 656 · 2017: 471 · 2016: 329 · 2015: 158
- FWCI
- 220.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- image captioning, recurrent neural networks, generative models, machine translation, image description generation, PASCAL dataset, Flickr30k, COCO dataset, BLEU evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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