CIDEr (Consensus-based Image Description Evaluation)
CIDEr stands for Consensus-based Image Description Evaluation. It is a metric specifically designed to assess the quality of captions generated by computer vision models for images. Unlike traditional metrics that may focus solely on exact word matches, CIDEr evaluates how well the generated captions align with human-written reference captions in terms of semantic content.
The CIDEr metric works by measuring the consensus between the generated captions and a set of reference captions. It does this by calculating the similarity of n-grams (contiguous sequences of n items from a given sample of text) between the generated caption and the reference captions. The n-grams are weighted based on their frequency in the set of reference captions, meaning that more common phrases contribute more to the score.
CIDEr is particularly useful in tasks such as image captioning because it accounts for variations in phrasing and expresses the degree to which the generated captions convey similar information to what human annotators would provide. A higher CIDEr score indicates a better alignment with human judgment, making it a popular choice for evaluating machine-generated text in visual tasks.
Overall, CIDEr is a critical tool in the field of natural language processing and computer vision, helping researchers and developers improve their models by providing a more nuanced understanding of caption quality.