Learning Spatiotemporal Features for Video Semantic Segmentation Using 3D Convolutional Neural Networks
Author:
Affiliation:
1. Beijing Institute of Technology, Zhuhai,School of Applied Science and Civil Engineering,Zhuhai,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10063336/10063352/10063480.pdf?arnumber=10063480
Reference20 articles.
1. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale;dosovitskiy;Computer Vision and Pattern Recognition,2020
2. Rethinking Atrous Convolution for Semantic Image Segmentation;chen;Computer Vision and Pattern Recognition,2017
3. Segmenter: Transformer for Semantic Segmentation
4. Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
5. A Stochastic Approximation Method
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1. A Multi-Modal Fusion 3D Semantic Segmentation Method;2023 3rd International Conference on Electronic Information Engineering and Computer Science (EIECS);2023-09-22
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