Chen et al., 2023 - Google Patents
SRCBTFusion-Net: An efficient fusion architecture via stacked residual convolution blocks and transformer for remote sensing image semantic segmentationChen et al., 2023
- Document ID
- 9433422445183285788
- Author
- Chen J
- Yi J
- Chen A
- Lin H
- Publication year
- Publication venue
- IEEE Transactions on Geoscience and Remote Sensing
External Links
Snippet
Convolutional neural network (CNN) and transformer-based self-attention models have their advantages in extracting local information and global semantic information, and it is a trend to design a model combining stacked residual convolution blocks (SRCBs) and transformer …
- 230000011218 segmentation 0 title abstract description 123
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- G06K9/46—Extraction of features or characteristics of the image
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- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
- G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
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- G06T2207/20112—Image segmentation details
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- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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- G—PHYSICS
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