CIDEr
E899060
CIDEr is an automatic evaluation metric designed to assess the quality of image captions by measuring their consensus with human-written descriptions.
All labels observed (5)
| Label | Occurrences |
|---|---|
| CIDEr canonical | 4 |
| CIDEr with damping | 1 |
| CIDEr-D | 1 |
| CIDEr: Consensus-based Image Description Evaluation | 1 |
| Consensus-based Image Description Evaluation | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003515 — 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: CIDEr Context triple: [Show and Tell: A Neural Image Caption Generator, evaluationMetric, CIDEr]
-
A.
Inception Score
Inception Score is a quantitative metric used to assess the quality and diversity of images generated by generative models by analyzing their classifiability and distribution across categories using a pretrained Inception network.
-
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.
MRPC
MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
-
D.
Maximal Marginal Relevance (MMR) for information retrieval and summarization
Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
-
E.
Fréchet Inception Distance
Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
- 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: CIDEr Target entity description: CIDEr is an automatic evaluation metric designed to assess the quality of image captions by measuring their consensus with human-written descriptions.
-
A.
Inception Score
Inception Score is a quantitative metric used to assess the quality and diversity of images generated by generative models by analyzing their classifiability and distribution across categories using a pretrained Inception network.
-
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.
MRPC
MRPC is the commonly used abbreviation for the Model Rules of Professional Conduct, a set of ethical standards governing lawyers’ professional behavior in the United States.
-
D.
Maximal Marginal Relevance (MMR) for information retrieval and summarization
Maximal Marginal Relevance (MMR) is an information retrieval and summarization technique that selects results by jointly maximizing relevance to a query while minimizing redundancy among the chosen items.
-
E.
Fréchet Inception Distance
Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
- F. None of above. chosen
Statements (45)
| Predicate | Object |
|---|---|
| instanceOf |
automatic evaluation metric
ⓘ
image captioning evaluation metric ⓘ image captioning evaluation metric ⓘ |
| aggregationMethod | averaging over reference captions ⓘ |
| basedOn | consensus among human reference captions ⓘ |
| commonlyReportedIn | image captioning research papers ⓘ |
| commonlyUsedWith | neural image captioning models ⓘ |
| comparesTo | human-written image descriptions ⓘ |
| correlatesWith | human caption quality assessments ⓘ |
| designedFor |
evaluating image caption quality
ⓘ
image captioning ⓘ |
| designedTo | reduce the effect of outlier n-grams ⓘ |
| domain | image description ⓘ |
| evaluatedOn |
MS COCO dataset
ⓘ
linked to:
MSCOCO
|
| evaluates | machine-generated image captions ⓘ |
| focusesOn | content similarity between candidate and reference captions ⓘ |
| fullName |
CIDEr with damping
ⓘ
linked to:
CIDEr
Consensus-based Image Description Evaluation ⓘ
linked to:
CIDEr
|
| hasAuthor |
C. Lawrence Zitnick
ⓘ
Devi Parikh ⓘ Ramakrishna Vedantam ⓘ |
| hasVariant |
CIDEr-D
ⓘ
linked to:
CIDEr
|
| higherIsBetter | true ⓘ |
| introducedAt |
ECCV 2015
ⓘ
linked to:
European Conference on Computer Vision
|
| introducedFor | automatic image caption evaluation ⓘ |
| introducedInField |
computer vision
ⓘ
natural language processing ⓘ |
| introducedYear | 2015 ⓘ |
| languageAgnostic | partially ⓘ |
| metricRange | 0 to 1 ⓘ |
| optimizedFor | correlation with human judgments ⓘ |
| paperTitle |
CIDEr: Consensus-based Image Description Evaluation
ⓘ
linked to:
CIDEr
|
| publicationType | research paper ⓘ |
| relatedMetric |
BLEU
ⓘ
METEOR ⓘ ROUGE-L ⓘ SPICE ⓘ |
| requires | multiple human reference captions per image ⓘ |
| status | standard benchmark metric for image captioning ⓘ |
| taskType | reference-based evaluation ⓘ |
| usedIn |
MS COCO Captioning Challenge
ⓘ
linked to:
COCO captioning challenge
|
| uses |
TF-IDF weighting of n-grams
ⓘ
cosine similarity over TF-IDF vectors ⓘ n-gram matching ⓘ term frequency-inverse document frequency weighting ⓘ |
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: CIDEr Description of subject: CIDEr is an automatic evaluation metric designed to assess the quality of image captions by measuring their consensus with human-written descriptions.
Referenced by (8)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: CIDEr
linked to: CIDEr