COIN: Counterfactual Image Generation for Visual Question Answering Interpretation

Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent and advanced. However, they are still error-prone when dealing with relatively complex questions. Therefore, it is important to un...

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Detalles Bibliográficos
Autores principales: Boukhers, Zeyd, Hartmann, Timo, Jürjens, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953790/
https://www.ncbi.nlm.nih.gov/pubmed/35336415
http://dx.doi.org/10.3390/s22062245

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8953790/
https://www.ncbi.nlm.nih.gov/pubmed/35336415
http://dx.doi.org/10.3390/s22062245

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