  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 .
