その CIDEr (Consensus-based 画像 Description 評価）スコアは 評価指標です specifically designed to assess the quality of image captions generated by 機械学習 models, particularly in the context of 画像キャプション tasks. It was developed to address limitations of other metrics like BLEU and ROUGE, which do not effectively capture the quality of descriptions based on human consensus.
CIDErスコアは、生成されたキャプションを人間が作成したリファレンスキャプションのセットと比較することで機能します。生成されたキャプションとリファレンスキャプション内のn-gram（連続したn個のアイテムのシーケンス）の合意を評価し、人間による注釈で頻繁に出現する単語の重要性を強調します。これは、使用される単語の正確さだけでなく、その関連性や適切さも考慮していることを意味します。
The CIDEr Score is calculated using a term frequency-inverse document frequency (TF-IDF) weighting scheme, which helps to ensure that the evaluation is sensitive to the uniqueness of the n-grams present in the reference captions. The resulting score ranges from 0 to 1, with higher scores indicating better alignment with human descriptions. This metric is particularly useful in tasks where the diversity and richness of language are important, such as in generating descriptive captions for images in multimedia applications.
Overall, the CIDEr Score serves as a valuable tool for researchers and developers in the field of 自然言語処理 and computer vision, as it helps to quantify the performance of image captioning models in a way that reflects human-like understanding and expression.