Redemption Score: An Evaluation Framework to Rank Image Captions While Redeeming Image Semantics and Language Pragmatics

• URL: http://arxiv.org/abs/2505.16180v1
• Date: Thu, 22 May 2025 03:35:12 GMT
• Title: Redemption Score: An Evaluation Framework to Rank Image Captions While Redeeming Image Semantics and Language Pragmatics
• Authors: Ashim Dahal, Ankit Ghimire, Saydul Akbar Murad, Nick Rahimi,
• Abstract summary: Redemption Score is a novel framework that ranks image captions by triangulating three complementary signals.<n>On the Flickr8k benchmark, Redemption Score achieves a Kendall-$tau$ of 56.43, outperforming twelve prior methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating image captions requires cohesive assessment of both visual semantics and language pragmatics, which is often not entirely captured by most metrics. We introduce Redemption Score, a novel hybrid framework that ranks image captions by triangulating three complementary signals: (1) Mutual Information Divergence (MID) for global image-text distributional alignment, (2) DINO-based perceptual similarity of cycle-generated images for visual grounding, and (3) BERTScore for contextual text similarity against human references. A calibrated fusion of these signals allows Redemption Score to offer a more holistic assessment. On the Flickr8k benchmark, Redemption Score achieves a Kendall-$\tau$ of 56.43, outperforming twelve prior methods and demonstrating superior correlation with human judgments without requiring task-specific training. Our framework provides a more robust and nuanced evaluation by effectively redeeming image semantics and linguistic interpretability indicated by strong transfer of knowledge in the Conceptual Captions and MS COCO datasets.

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