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A consensus-based metric for evaluating image captions by comparing generated descriptions to multiple human references using TF-IDF weighted n-grams.
DESCRIPTION
CIDEr (Consensus-based Image Description Evaluation) measures similarity between generated captions and sets of human references. It uses TF-IDF weighted n-grams to capture consensus across annotators. Designed for image captioning, CIDEr correlates better with human judgment than some earlier metrics. It is commonly used with the COCO Captions benchmark. Variants like CIDEr-D apply damping to reduce overfitting to common phrases.
ALTERNATIVE DEFINITIONS
arXiv
We propose CIDEr, a novel evaluation metric for image description that captures consensus of human judgements by measuring TF-IDF weighted n-gram similarity to a set of reference sentences.
Defines CIDEr’s consensus-driven TF-IDF n-gram approach.
COCO Dataset
CIDEr computes the similarity of a candidate caption to a set of human-written reference captions, emphasizing consensus through TF-IDF weighted n-grams.
Highlights role within the COCO caption evaluation suite.
RELATED TERMS
METEOR Captioning COCO Captions N-gram Term Frequency–Inverse Document Frequency Consensus Evaluation Metrics
COMPETING TERMS
RELATIONS
- Broader: Evaluation Metrics
KNOWLEDGE AREAS
AI Data Science Testing & Validation Data Quality
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer BI Specialist
EXTERNAL SOURCES
- Wikipedia - Image captioning
- arXiv - Original CIDEr metric paper
- COCO Dataset - COCO caption evaluation suite
- COCO Caption - Reference implementation of caption metrics
TOP QUESTIONS
Why use CIDEr for image caption evaluation?
CIDEr captures consensus across multiple references, improving correlation with human judgment compared to single-reference metrics.
Category: Relevancy
How does CIDEr differ from BLEU?
BLEU emphasizes precision of n-grams, while CIDEr uses TF-IDF weights and multiple references to reflect consensus quality.
Category: Alternatives
What is CIDEr-D?
CIDEr-D introduces damping and clipping to mitigate overfitting to common n-grams, improving robustness.
Category: Capabilities
What are common mistakes with CIDEr?
Using too few references, ignoring tokenization rules, or misapplying TF-IDF weights can skew results.
Category: Mistakes
Does higher CIDEr always mean better captions?
Generally yes, but it can favor safe phrasing; human review remains important for nuance and correctness.
Category: Misunderstanding
How to apply CIDEr in practice?
Use standard implementations, ensure proper tokenization, and evaluate across datasets like COCO with multiple references.
Category: Getting Started
What are the risks of relying solely on CIDEr?
Over-optimization to metric-specific artifacts and ignoring semantic correctness are risks; use multiple metrics and human evaluation.
Category: Risk
What is currently a hot topic around CIDEr?
Combining metrics, semantic evaluation, and human-in-the-loop methods to improve caption quality beyond surface similarity.
Category: Topic
How does CIDEr handle paraphrasing?
TF-IDF weighting helps, but paraphrases may still be under-rewarded; multiple references mitigate this.
Category: Opinion