| Challenge: | Current metrics for imagetext similarity tend to be insensitive to the text's purpose. |
| Approach: | They propose to use a model that assigns higher scores to descriptions than captions . they use parameter efficient fine-tuning and a loss objective to shed light on the distinction . |
| Outcome: | The proposed model correlates with the judgements of blind and low-vision people while preserving transfer capabilities and sheds light on the caption–description distinction. |
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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. |
SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation (2025.emnlp-main)
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| Challenge: | N-gram-based evaluation metrics are unreliable due to low correlation to human judgments. |
| Approach: | They propose a metric that rewards correct details and penalizes incorrect ones. |
| Outcome: | The proposed metric matches the performance of open-source LLM-based metrics in correlation to human judgments while being far more efficient. |
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. |
| Outcome: | The proposed evaluation strategies are based on machine-translated and human judgements. |
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 . |
CLIP4IDC: CLIP for Image Difference Captioning (2022.aacl-short)
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| Challenge: | Conventional approaches learn an IDC model with a pre-trained and usually frozen visual feature extractor. |
| Approach: | They propose to transfer a CLIP model to the downstream IDC task to address two major issues: (1) a large domain gap exists between the pre-training datasets used for training such a visual feature extractor; (2) the visual feature extraction often does not effectively encode the visual changes between two images. |
| Outcome: | Experiments on three IDC benchmark datasets show the proposed model performs well. |
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. |
Fine-grained Image Captioning with CLIP Reward (2022.findings-naacl)
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| Challenge: | Modern image captioning models are usually trained with text similarity objectives . reference captions often describe only the most salient objects in images . |
| Approach: | They propose to use CLIP to calculate multi-modal similarity and use it as a reward function . they propose a simple finetuning strategy to improve grammar that does not require extra text annotation. |
| Outcome: | The proposed model generates more distinctive captions than the CIDEroptimized model on text-to-image retrieval and fineCapEval. |
Pragmatic Inference with a CLIP Listener for Contrastive Captioning (2023.findings-acl)
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| Challenge: | a new method for contrastive captioning generates discriminative captions that distinguish target images from very similar alternative distractor images. |
| Approach: | They propose a pragmatic inference procedure that formulates captioning as a reference game between a speaker and a listener. |
| Outcome: | The proposed method outperforms previous methods for discriminative captioning by 11% to 15% accuracy in human evaluations. |
Concadia: Towards Image-Based Text Generation with a Purpose (2022.emnlp-main)
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| Challenge: | Existing models fail to generate fluent, truthful text, despite excellent results on benchmark datasets . current systems fail to produce texts that are useful in practice, authors argue . |
| Approach: | They propose to distinguish descriptions from captions based on their communicative roles . descriptions focus on visual features and are meant to replace an image . authors characterize commonalities and differences between descriptions and captions in a Wikipedia corpus . |
| Outcome: | The proposed model can generate fluent, truthful texts in a wide range of scenarios . it can also generate captions that are used to make an image accessible to users who can't see them . |
A Conformal Risk Control Framework for Granular Word Assessment and Uncertainty Calibration of CLIPScore Quality Estimates (2025.findings-acl)
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| Challenge: | Existing caption evaluation metrics lack granular assessments for errors within captions . lack of uncertainty quantification can give way to misleading scores, reducing user trust . |
| Approach: | They propose a conformal risk control framework to generate and calibrate CLIPScore distributions . they use a model-agnostic conformal framework to detect erroneous words . |
| Outcome: | The proposed method detects erroneous words while providing formal guarantees aligned with desired risk levels. |