Show, Attend and Tell
E899062
Show, Attend and Tell is a neural image captioning model that introduced visual attention mechanisms to dynamically focus on different parts of an image while generating descriptive text.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Show, Attend and Tell canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003527 — 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, Attend and Tell Context triple: [Show and Tell: A Neural Image Caption Generator, influence, Show, Attend and Tell]
-
A.
Show and Prove
Show and Prove is the first full-length studio album by American rapper Wiz Khalifa, showcasing his early Pittsburgh-rooted hip hop style.
-
B.
Tell Your Friends
"Tell Your Friends" is a jazz-fusion album by the instrumental ensemble Snarky Puppy that helped establish their reputation for complex arrangements and energetic, groove-driven performances.
-
C.
Tell A Story
"Tell A Story" is a song by the American rock band Blue Collar.
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D.
The Show of Shows
The Show of Shows is a 1929 Warner Bros. all-star revue film from the early sound era, featuring numerous studio contract players in musical and comedy sketches.
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E.
Welcome to the Show
"Welcome to the Show" is a song by the American rock band The Donnas from their album "Donuts."
- 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, Attend and Tell Target entity description: Show, Attend and Tell is a neural image captioning model that introduced visual attention mechanisms to dynamically focus on different parts of an image while generating descriptive text.
-
A.
Show and Prove
Show and Prove is the first full-length studio album by American rapper Wiz Khalifa, showcasing his early Pittsburgh-rooted hip hop style.
-
B.
Tell Your Friends
"Tell Your Friends" is a jazz-fusion album by the instrumental ensemble Snarky Puppy that helped establish their reputation for complex arrangements and energetic, groove-driven performances.
-
C.
Tell A Story
"Tell A Story" is a song by the American rock band Blue Collar.
-
D.
The Show of Shows
The Show of Shows is a 1929 Warner Bros. all-star revue film from the early sound era, featuring numerous studio contract players in musical and comedy sketches.
-
E.
Welcome to the Show
"Welcome to the Show" is a song by the American rock band The Donnas from their album "Donuts."
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
attention-based model
ⓘ
deep learning model ⓘ neural image captioning model ⓘ |
| affiliationContext | University of Montreal NERFINISHED ⓘ |
| approach | encoder-decoder architecture ⓘ |
| citationImpact | highly cited in vision-language research ⓘ |
| demonstrates | visualization of attention maps over image regions ⓘ |
| domain |
computer vision
ⓘ
natural language processing ⓘ |
| evaluationDataset |
Flickr30k
NERFINISHED
ⓘ
Flickr8k NERFINISHED ⓘ MSCOCO NERFINISHED ⓘ |
| focusesOn | different parts of an image during caption generation ⓘ |
| goal | generate descriptive natural language captions for images ⓘ |
| hasAuthor |
Aaron Courville
NERFINISHED
ⓘ
Jimmy Ba NERFINISHED ⓘ Kelvin Xu NERFINISHED ⓘ Kyunghyun Cho NERFINISHED ⓘ Richard Zemel NERFINISHED ⓘ Ruslan Salakhutdinov NERFINISHED ⓘ Ryan Kiros NERFINISHED ⓘ Yoshua Bengio NERFINISHED ⓘ |
| hasFullName | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention NERFINISHED ⓘ |
| improvesOver | non-attention image captioning models ⓘ |
| influenced | transformer-based image captioning models ⓘ |
| inspired | later attention-based vision-language models ⓘ |
| introduced | visual attention mechanism for image captioning ⓘ |
| language | English captions ⓘ |
| learningParadigm | supervised learning ⓘ |
| property | dynamically attends to image regions at each word step ⓘ |
| publicationType | conference paper ⓘ |
| publicationYear | 2015 ⓘ |
| publishedIn | International Conference on Machine Learning NERFINISHED ⓘ |
| publishedInShort | ICML NERFINISHED ⓘ |
| task | image caption generation ⓘ |
| uses |
CNN features as image encoder
ⓘ
RNN language model ⓘ alignment model between image regions and words ⓘ hard attention ⓘ soft attention ⓘ visual attention ⓘ |
| usesDecoder |
LSTM
NERFINISHED
ⓘ
recurrent neural network ⓘ |
| usesEncoder | convolutional neural network ⓘ |
| usesTrainingMethod |
REINFORCE for hard attention approximation
ⓘ
backpropagation through time ⓘ stochastic gradient descent ⓘ |
| usesTrainingObjective | maximum likelihood estimation ⓘ |
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, Attend and Tell Description of subject: Show, Attend and Tell is a neural image captioning model that introduced visual attention mechanisms to dynamically focus on different parts of an image while generating descriptive text.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.