Show and Tell: A Neural Image Caption Generator
E260056
"Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Show and Tell: A Neural Image Caption Generator canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T2373760 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Show and Tell: A Neural Image Caption Generator Context triple: [Oriol Vinyals, notableWork, Show and Tell: A Neural Image Caption Generator]
-
A.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
-
B.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
-
C.
PixelCNN
PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
-
D.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
E.
Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Show and Tell: A Neural Image Caption Generator Target entity description: "Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
-
A.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
-
B.
CLIP
CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
-
C.
PixelCNN
PixelCNN is a deep generative model that uses convolutional neural networks with autoregressive masking to model and generate images pixel by pixel.
-
D.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
E.
Inception architecture
The Inception architecture is a deep convolutional neural network design that introduced parallel multi-scale processing modules to achieve state-of-the-art image recognition performance with improved computational efficiency.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
deep learning model
ⓘ
image captioning model ⓘ neural network-based system ⓘ research paper ⓘ |
| abbreviation | Show and Tell ⓘ |
| application |
assistive technologies for the visually impaired
ⓘ
automatic image description ⓘ image retrieval ⓘ |
| approach | end-to-end neural network ⓘ |
| architectureType | CNN-RNN ⓘ |
| author |
Alexander Toshev
ⓘ
Dumitru Erhan ⓘ Oriol Vinyals ⓘ Samy Bengio ⓘ |
| basedOn | encoder-decoder architecture ⓘ |
| citationType | highly cited paper ⓘ |
| decoder | recurrent neural network ⓘ |
| encoder | convolutional neural network ⓘ |
| evaluationMetric |
BLEU
ⓘ
CIDEr ⓘ METEOR ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ natural language processing ⓘ |
| firstAuthor | Oriol Vinyals ⓘ |
| influence |
Neural image captioning research
ⓘ
Show, Attend and Tell ⓘ |
| input | image ⓘ |
| language | English ⓘ |
| learningParadigm | sequence-to-sequence learning ⓘ |
| notableFor | pioneering end-to-end neural image captioning ⓘ |
| organization | Google ⓘ |
| output | natural-language description ⓘ |
| publishedIn |
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ⓘ
surface form:
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
|
| task | image captioning ⓘ |
| title | Show and Tell: A Neural Image Caption Generator self-link ⓘ |
| trainingMethod | supervised learning ⓘ |
| trainingObjective | maximize likelihood of correct caption ⓘ |
| uses |
LSTM networks
ⓘ
surface form:
Long Short-Term Memory network
convolutional neural network ⓘ recurrent neural network ⓘ |
| usesDataset |
Flickr30k
ⓘ
Flickr8k ⓘ MSCOCO ⓘ |
| usesLossFunction | log-likelihood loss ⓘ |
| usesPretrainedModel | ImageNet CNN ⓘ |
| venue |
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ⓘ
surface form:
CVPR
|
| year | 2014 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Show and Tell: A Neural Image Caption Generator Description of subject: "Show and Tell: A Neural Image Caption Generator" is a pioneering deep learning model that automatically generates natural-language descriptions for images by combining convolutional and recurrent neural networks.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
Show and Tell: A Neural Image Caption Generator
→
title
→
Show and Tell: A Neural Image Caption Generator
self-link
ⓘ