JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A54UI3UDQ" target="_blank" >RIV/00216208:11320/23:54UI3UDQ - isvavai.cz</a>
Result on the web
<a href="http://arxiv.org/abs/2311.04192" target="_blank" >http://arxiv.org/abs/2311.04192</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.48550/arXiv.2311.04192" target="_blank" >10.48550/arXiv.2311.04192</a>

Result language
angličtina
Original language name
JaSPICE: Automatic Evaluation Metric Using Predicate-Argument Structures for Image Captioning Models
Original language description
"Image captioning studies heavily rely on automatic evaluation metrics such as BLEU and METEOR. However, such n-gram-based metrics have been shown to correlate poorly with human evaluation, leading to the proposal of alternative metrics such as SPICE for English; however, no equivalent metrics have been established for other languages. Therefore, in this study, we propose an automatic evaluation metric called JaSPICE, which evaluates Japanese captions based on scene graphs. The proposed method generates a scene graph from dependencies and the predicate-argument structure, and extends the graph using synonyms. We conducted experiments employing 10 image captioning models trained on STAIR Captions and PFN-PIC and constructed the Shichimi dataset, which contains 103,170 human evaluations. The results showed that our metric outperformed the baseline metrics for the correlation coefficient with the human evaluation."
Czech name
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Czech description
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Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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