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. |
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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. |
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InfoMetIC: An Informative Metric for Reference-free Image Caption Evaluation (2023.acl-long)
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| Challenge: | Existing image captioning metrics provide a single score to measure caption qualities, which are less explainable and informative. |
| Approach: | They propose an Informative Metric for Reference-free Image Caption evaluation to support this feedback . they propose to provide a text precision score, a vision recall score and an overall quality score . |
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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 . |
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Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics (2020.acl-main)
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| Challenge: | Existing methods for judging metrics are sensitive to the translations used for evaluation, leading to falsely confident conclusions about a metric’s efficacy. |
| Approach: | They propose a method for thresholding performance improvement under an automatic metric against human judgements by using a pairwise system ranking method. |
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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. |
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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. |
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Quality Estimation for Image Captions Based on Large-scale Human Evaluations (2021.naacl-main)
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| Challenge: | a problem with automatic image captioning is that it produces low quality captions when used in the wild. |
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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. |
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Evaluation of Multilingual Image Captioning: How far can we get with CLIP models? (2025.findings-naacl)
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| Challenge: | Existing approaches to evaluate image captions are English-centric, despite improvements in the CLIPScore metric . however, there are no available benchmarks for multilingual captioning evaluation . |
| Approach: | They propose to use machine-translated and machine-repurposed datasets to evaluate CLIPScore variants in multilingual settings. |
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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 . |
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