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 https://github.com/ DSBA-Lab/ECO .

翻译：本报告介绍了DSBA LAB团队提出的ECO（集成CLIP分数与共识分数）流程，这是一种用于评估和排序给定图像字幕的新框架。ECO能够选择最准确描述图像的字幕，其核心在于融合两项指标：衡量图像与字幕语义对齐的集成CLIP分数，以及评估字幕关键性的共识分数。运用该框架，我们在CVPR 2024零样本图像字幕评估新前沿（NICE）研讨会挑战赛的字幕重排序评估任务中取得显著成果——具体而言，基于CIDEr指标位列第三，SPICE和METEOR指标均获第二，ROUGE-L及所有BLEU指标均夺得第一。ECO框架的代码与配置已开源至https://github.com/DSBA-Lab/ECO。