Multi-Attention-Network for Semantic Segmentation of High-Resolution Remote Sensing Images
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Summary
A novel attention mechanism named kernel attention with linear complexity is proposed to alleviate the high computational demand of attention and integrate local feature maps extracted by ResNeXt-101 with their corresponding global dependencies, and adaptively signalize interdependent channel maps.
- Type
- article
- Published
- 2020-09-03
- Cited by
- 31
- References
- 60
- OpenAlex
- https://openalex.org/W3083445449
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:265039168
Keywords
Computer science, Segmentation, Encoder, Feature (linguistics), Artificial intelligence
References
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- Deep Residual Learning for Image Recognition
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- SEMANTIC SEGMENTATION OF AERIAL IMAGES WITH AN ENSEMBLE OF CNNS
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- Pyramid Scene Parsing Network
Cited by
- Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
- Transformer Meets Convolution: A Bilateral Awareness Net-work for Semantic Segmentation of Very Fine Resolution Ur-ban Scene Images
- ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
- Semantic Segmentation and Edge Detection - Approach to Road Detection in Very High Resolution Satellite Images
- Edge Guided Context Aggregation Network for Semantic Segmentation of Remote Sensing Imagery
- Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture
- River Extraction from Remote Sensing Images in Cold and Arid Regions Based on Attention Mechanism
- Thyroid Nodule Segmentation in Ultrasound Image Based on Information Fusion of Suggestion and Enhancement Networks
- Defective sewing stitch semantic segmentation using DeeplabV3+ and EfficientNet
- Category-Wise Fusion and Enhancement Learning for Multimodal Remote Sensing Image Semantic Segmentation
- Cut the peaches: image segmentation for utility pattern mining in food processing
- CSRL-Net: contextual self-rasterization learning network with joint weight loss for remote sensing image semantic segmentation
- Cloud and Snow Neural Network Segmentation using the Electro-L No. 2 satellite low-resolution data
- HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait
- F2M: Ensemble-based uncertainty estimation model for fire detection in indoor environments
- FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation
- An effective dual encoder network with a feature attention large kernel for building extraction
- Point-Supervised Semantic Segmentation of Natural Scenes via Hyperspectral Imaging
- Drivable path detection for a mobile robot with differential drive using a deep Learning based segmentation method for indoor navigation
- Early detection of downy mildew in vineyards using deep neural networks for semantic segmentation
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