Ming Jiang, Qiuyuan Huang, Lei Zhang, Xin Wang, Pengchuan Zhang, Zhe Gan, Jana Diesner, Jianfeng Gao
| Challenge: | Existing metrics based on text-level comparisons fail to assess the quality of captions produced by machines. |
| Approach: | They propose to use a machine-learned text-image grounding model to measure the accuracy of machine-generated captions and their correlation with human judgments. |
| Outcome: | The proposed metric has higher consistency with human judgments and is more accurate than existing metrics. |
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Learning-based Composite Metrics for Improved Caption Evaluation (P18-3)
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| Challenge: | Existing image captioning metrics focus on linguistic aspects and do not match human judgements at sentence-level. |
| Approach: | They propose to incorporate lexical and semantic metrics as features to capture adequacy and fluency of captions at different linguistic levels. |
| Outcome: | The proposed framework captures adequacy and fluency of captions at different linguistic levels. |
CLAIR: Evaluating Image Captions with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing measures for image caption evaluation fail to capture dimensions of similarity . a novel method that leverages the zero-shot language modeling capabilities of large language models (LLMs) demonstrates a stronger correlation with human judgments of caption quality compared to existing measures. |
| Approach: | They propose a method that leverages the zero-shot language modeling capabilities of large language models to evaluate captions. |
| Outcome: | The proposed method shows a stronger correlation with human judgments of caption quality compared to other measures. |
REO-Relevance, Extraness, Omission: A Fine-grained Evaluation for Image Captioning (D19-1)
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| Challenge: | Existing metrics for image captioning evaluation provide an overall quality score, which is difficult to infer specific description errors. |
| Approach: | They propose a fine-grained evaluation method REO for automatically measuring the performance of image captioning systems. |
| Outcome: | The proposed method achieves higher consistency with human judgments and provides more intuitive evaluation results than other metrics. |
CLIPScore: A Reference-free Evaluation Metric for Image Captioning (2021.emnlp-main)
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| Challenge: | Image captioning relies on reference-based automatic evaluations, but references are expensive to collect and comparing against multiple human-authored captions is insufficient. |
| Approach: | They propose a reference-free metric that can be used for automatic caption evaluation without references. |
| Outcome: | The proposed model outperforms existing metrics on image-text compatibility and a reference-augmented version achieves even higher correlation with human judgements. |
Transparent Human Evaluation for Image Captioning (2022.naacl-main)
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Jungo Kasai, Keisuke Sakaguchi, Lavinia Dunagan, Jacob Morrison, Ronan Le Bras, Yejin Choi, Noah A. Smith
| Challenge: | Recent work has demonstrated that image captioning is a complex task that requires a large amount of human input. |
| Approach: | They develop a human evaluation protocol for image captioning models based on machine- and human-generated captions on the MSCOCO dataset. |
| Outcome: | The proposed model improves CLIPScore, a recent metric that uses image features, and improves human judgments because it is more sensitive to recall. |
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations (2025.findings-acl)
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| Challenge: | Existing studies on explainable evaluation metrics generate explanations without standardized criteria and the overall quality of the generated explanations remains unverified. |
| Approach: | They propose a reference-free evaluation metric that provides structured explanations based on fluency, relevance, and descriptiveness. |
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VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models (2024.emnlp-main)
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| Challenge: | Existing metrics, such as CLIP, measure the semantic alignment between single prompts and their corresponding images, but they fail to evaluate a model’s generalizability across a broad spectrum of textual inputs. |
| Approach: | They propose a metric that leverages the power of Large Language Models to sample from the visual text domain and assess its generalizability. |
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FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model (2024.acl-long)
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| Challenge: | Existing image captioning evaluation metrics do not provide an explanation for the assigned numerical score. |
| Approach: | They propose an explainable reference-free metric to provide an explanation for captions . they introduce score smoothing to align as closely as possible with human judgment . |
| Outcome: | The proposed metric achieves high correlations with human judgment across image captioning evaluation benchmarks and is publicly available at https://github.com/Yebin46/FLEUR. |
An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics (2024.findings-eacl)
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| Challenge: | Recent studies have proposed reference-free evaluations of image captions . however, these approaches are restrictive and favor captions with similar vocabulary but different meanings. |
| Approach: | They propose to use reference-free metrics to evaluate image captions . they propose to combine lexical overlap and semantics to identify fine-grained errors . |
| Outcome: | The proposed metrics struggle to identify fine-grained errors, the authors show . CLIPScore, UMIC, and PAC-S are sensitive to variations in image-relevant objects mentioned in the caption . |
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)
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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen
| Challenge: | Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected. |
| Approach: | They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans . |
| Outcome: | The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test . |