BEiTScore: Reference-free Image Captioning Evaluation with an Efficient Cross-Encoder Model
BEiTScore is a new reference-free image captioning evaluation metric based on a lightweight cross-encoder initialized from a VQA model checkpoint, using adversarial LLM-based data augmentation to achieve state-of-the-art performance with high efficiency.
[2605.21728] BEiTScore: Reference-free Image Captioning Evaluation with an Efficient Cross-Encoder Model
[Submitted on 20 May 2026]
Title:BEiTScore: Reference-free Image Captioning Evaluation with an Efficient Cross-Encoder Model
View a PDF of the paper titled BEiTScore: Reference-free Image Captioning Evaluation with an Efficient Cross-Encoder Model, by Gon\c{c}alo Gomes and 2 other authors
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Abstract:Image captioning evaluation remains a significant challenge, as vision-language models evolve toward more challenging capabilities such as generating long-form and context-rich descriptions. State-of-the-art evaluation metrics involve extensive computational costs associated with the use of Large Language Models (LLMs) as judges, or instead suffer from the limitations of standard CLIP-based encoders, such as strict token limits, lack of fine-grained sensitivity, or lack of compositional generalization by treating captions as ``bags-of-words.'' We propose a new learned metric that tackles the aforementioned challenges, based on a lightweight cross-encoder that is initialized from a visual question-answering model checkpoint, balancing a strong weight initialization with computational efficiency. Our training scheme uses a carefully assembled data mixture for supervised learning, featuring adversarial LLM-based data augmentations to enhance model sensitivity to fine-grained visual-linguistic errors. We also introduce a new benchmark designed to assess detailed captioning evaluation across diverse scenarios. Experimental results demonstrate that the proposed metric achieves state-of-the-art performance while maintaining the efficiency required for large-scale benchmarking, quality-aware decoding, or reward guidance.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2605.21728 [cs.CV]
(or arXiv:2605.21728v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.21728
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gonçalo Emanuel Cavaco Gomes [view email] [v1] Wed, 20 May 2026 20:43:38 UTC (1,113 KB)
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