This report presents the ECO (Ensembled Clip score and cOnsensus score)
pipeline from team DSBA LAB, which is a new framework used to evaluate and rank
captions for a given image. ECO selects the most accurate caption describing
image. It is made possible by combining an Ensembled CLIP score, which
considers the semantic alignment between the image and captions, with a
Consensus score that accounts for the essentialness of the captions. Using this
framework, we achieved notable success in the CVPR 2024 Workshop Challenge on
Caption Re-ranking Evaluation at the New Frontiers for Zero-Shot Image
Captioning Evaluation (NICE). Specifically, we secured third place based on the
CIDEr metric, second in both the SPICE and METEOR metrics, and first in the
ROUGE-L and all BLEU Score metrics. The code and configuration for the ECO
framework are available at this https URL DSBA-Lab/ECO .

DSBA LAB 团队提出了一种名为 ECO（Ensembled Clip score and cOnsensus score）的新框架用于评估和排名给定图像的字幕，通过将考虑图像和字幕的语义对齐的 Ensembled Clip 得分与考虑字幕重要性的 Consensus 得分相结合，选择最准确的描述图像的字幕。在 CVPR 2024 研讨会上举办的 Caption Re-ranking Evaluation 比赛中，我们在 CIDEr 评价指标上获得了第三名，在 SPICE 和 METEOR 指标上获得了第二名，在 ROUGE-L 和全部 BLEU 评价指标上获得了第一名。ECO 框架的代码和配置可在 DSBA-Lab/ECO 的 https 链接中找到。