Page 67 - Kỷ yếu hội thảo khoa học lần thứ 12 - Công nghệ thông tin và Ứng dụng trong các lĩnh vực (CITA 2023)

Duy Tran, Thang Le, Khoa Tran, Hoang Le, Cuong Do, Thanh Ha 51

team can have access to models pre-trained on facial datasets, the performance is likely
to be better.
Another potential avenue for future work is to explore other transformer variants, such
as Swin-Transformer, which has shown promising results in other computer vision tasks.
It would be interesting to investigate whether DeiT (Data-efficient Image Transformers)
could achieve even higher accuracy for face beauty evaluation than ViT [13].
Another area of future research could be to explore ensembling methods for
combining multiple models. Ensembling has been shown to be an effective way to
improve the accuracy of deep learning models by combining the strengths of multiple
models. It would be interesting to investigate whether ensembling ViT with other
models, such as ResNet or VGG, could achieve even higher accuracy for face beauty
evaluation.
Overall, there are several promising directions for future research in this area, and
we hope that our work will inspire further investigation into the use of transformer-
based models for evaluating human beauty.

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ISBN: 978-604-80-8083-9 CITA 2023