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Conv-PVT: a fusion architecture of convolution and pyramid vision transformer

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Abstract

Vision Transformer (ViT) has fully exhibited the potential of Transformer in computer vision domain. However, the computational complexity is proportional to the input dimension which is a constant value for Transformer. Therefore, training a vision transformer network is extremely memory expensive, where a large number of intermediate activation functions and parameters are involved to compute the gradients during back-propagation. In this paper, we propose Conv-PVT (Convolution blocks + Pyramid Vision Transformer) to improve the overall performance of vision transformer. Especially, we deploy simple convolution blocks in the first layer to reduce the memory footprint by down-sampling the input. Extensive experiments (including image classification, object detection and segmentation) have been carried out on ImageNet-1k, COCO and ADE20k datasets to test the accuracy, training time, memory occupation and robustness of our model. The results demonstrate that Conv-PVT achieves comparable performances with the original PVT and outperforms ResNet and ResNetXt for some downstream vision tasks. But it shortens 60% of the training time and reduces 42% GPU (Graphics Processing Unit) memory occupation, realizing twice the inference speed of PVT.

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Data Availability Statement

All data included in this study are available upon request by contact with the corresponding author.

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  1. Department of Computer Science, Sichuan University, Chengdu, China

    Xin Zhang & Yi Zhang

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  1. Xin Zhang
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  2. Yi Zhang
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Correspondence to Yi Zhang.

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Zhang, X., Zhang, Y. Conv-PVT: a fusion architecture of convolution and pyramid vision transformer. Int. J. Mach. Learn. & Cyber. 14, 2127–2136 (2023). https://doi.org/10.1007/s13042-022-01750-0

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