Technologies, Vol. 10, Pages 2: Artwork Style Recognition Using Vision Transformers and MLP Mixer

Technologies doi: 10.3390/technologies10010002

Lazaros Alexios Iliadis
Spyridon Nikolaidis
Panagiotis Sarigiannidis
Shaohua Wan
Sotirios K. Goudos

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies.

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