Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to the convolutional neural network (CNN)-based models. However, ViTs mainly designed for image classification will generate single-scale low-resolution representations, which makes dense prediction t...

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Bibliographic Details

Published in:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 12084 - 12093
Main Authors:	Gu, Jiaqi, Kwon, Hyoukjun, Wang, Dilin, Ye, Wei, Li, Meng, Chen, Yu-Hsin, Lai, Liangzhen, Chandra, Vikas, Pan, David Z.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.01.2022
Subjects:	Architecture Computer architecture Computer vision Deep learning architectures and techniques; Efficient learning and inferences; Representation learning; Segmentation grouping and shape analysis Object detection Redundancy Representation learning Semantics
ISSN:	1063-6919
Online Access:	Get full text
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