Vision Transformers (ViT) and other Transformer-based architectures for image classification have achieved promising performances in the last two years. However, ViT-based models require large datasets, memory, and computational power to obtain state-of-the-art results compared to more traditional architectures. The generic ViT model, indeed, maintains a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. With the goal of increasing the efficiency of Transformer-based models, we explore the application of a 2D max-pooling operator on the outputs of Transformer encoders. We conduct extensive experiments on the CIFAR-100 dataset and the large ImageNet dataset and consider both accuracy and efficiency metrics, with the final goal of reducing the token sequence length without affecting the classification performance. Experimental results show that bidimensional downsampling can outperform previous classification approaches while requiring relatively limited computation resources.

Investigating Bidimensional Downsampling in Vision Transformer Models / Bruno, Paolo; Amoroso, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita. - 13232:(2022), pp. 287-299. ((Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a Lecce, Italy nel 23 - 27 May 2022 [10.1007/978-3-031-06430-2_24].

Investigating Bidimensional Downsampling in Vision Transformer Models

Amoroso, Roberto;Cornia, Marcella;Cascianelli, Silvia;Baraldi, Lorenzo;Cucchiara, Rita
2022

Abstract

Vision Transformers (ViT) and other Transformer-based architectures for image classification have achieved promising performances in the last two years. However, ViT-based models require large datasets, memory, and computational power to obtain state-of-the-art results compared to more traditional architectures. The generic ViT model, indeed, maintains a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. With the goal of increasing the efficiency of Transformer-based models, we explore the application of a 2D max-pooling operator on the outputs of Transformer encoders. We conduct extensive experiments on the CIFAR-100 dataset and the large ImageNet dataset and consider both accuracy and efficiency metrics, with the final goal of reducing the token sequence length without affecting the classification performance. Experimental results show that bidimensional downsampling can outperform previous classification approaches while requiring relatively limited computation resources.
21st International Conference on Image Analysis and Processing, ICIAP 2022
Lecce, Italy
23 - 27 May 2022
13232
287
299
Bruno, Paolo; Amoroso, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita
Investigating Bidimensional Downsampling in Vision Transformer Models / Bruno, Paolo; Amoroso, Roberto; Cornia, Marcella; Cascianelli, Silvia; Baraldi, Lorenzo; Cucchiara, Rita. - 13232:(2022), pp. 287-299. ((Intervento presentato al convegno 21st International Conference on Image Analysis and Processing, ICIAP 2022 tenutosi a Lecce, Italy nel 23 - 27 May 2022 [10.1007/978-3-031-06430-2_24].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1268738
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