Improving Evaluation Metrics for Vision-and-Language Models

Priberam Labs

Instituto Superior Técnico, Anfiteatro PA2

Abstract:Evaluating image captions is essential for ensuring both linguistic fluency and accurate semantic alignment with visual content. While reference-free metrics such as CLIPScore have advanced automated caption evaluation, most existing work on learned evaluation metrics remains limited to pointwise English-centric assessments, with significant gaps in terms of reliability, interpretability, and multilingual inclusivity of vision-and-language evaluation metrics. In this seminar session I will explore extensions of current English-centric benchmarks to a multilingual scenario promoting the development of more inclusive frameworks. Additionally I will present two extensions from CLIPScore metric aiming to improve its interpretability and reliability in real world applications. Leveraging a model-agnostic conformal risk control framework, I will explore the calibration of CLIPScore distributions values for task-specific control variables tackling both granular assessment for individual word errors within captions, and the calibration of these raw distribution scores producing a more reliable interval for captioning evaluation by improving the correlation between uncertainty estimations and prediction errors. Bio:Gonçalo Gomes received a MSc degree in Data Science and Engineering, from Instituto Superior Técnico, Universidade de Lisboa. He is currently a second-year PhD student at the same institution and a junior researcher at the Human Language Technologies Lab of INESC-ID and also at SARDINE Labs of Instituto de Telecomunicações (IT). His research interests focus on developing more informative and trustworthy evaluation frameworks for vision-and-language applications, particularly envisioning a more inclusive AI frameworks for non-English environments.<a href="https://www.priberam.com/" title="https://www.priberam.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">www.priberam.com</a>

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Improving Evaluation Metrics for Vision-and-Language Models

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Improving Evaluation Metrics for Vision-and-Language Models

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