Abstract
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 .
Abstract (translated)
本报告介绍了一个名为ECO（集成剪辑分数和共识分数）的框架，该框架用于评估和排名给定图像的 caption。ECO 选择最准确的图像描述。这是通过将集成 CLIP 分数（考虑图像与捕获的文本之间的语义对齐）与共识分数（考虑捕获的文本的重要性）相结合而实现的。使用这个框架，我们在 CVPR 2024 工作站挑战中对捕捉重新排名评估的新前沿（NICE）取得了显著的成功。具体来说，我们在 CIDEr 指标上获得了第三名的成绩，在 SPICE 和 METEOR 指标上排名第二，而在 ROUGE-L 和所有 BLEU 分数指标上排名第一。ECO 框架的代码和配置可用於此链接 DSBA-Lab/ECO。
URL
https://arxiv.org/abs/2405.01028
