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CAGs-Net: A Novel Adjacent-Context Network With Channel Attention Gate for 3D Brain Tumor Image Segmentation. [PDF]
Int J Biomed ImagingYe Q, Shi Y, Guo S.
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Improved Grain Boundary Reconstruction Method Based on Channel Attention Mechanism. [PDF]
Materials (Basel)Duan X +5 more
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Point-Level Fusion and Channel Attention for 3D Object Detection in Autonomous Driving. [PDF]
Sensors (Basel)Shen J, Fang Z, Huang J.
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FcaNet: Frequency Channel Attention Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021 ICCV2021
Fei Wu, Pengyi Zhang
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Mixed Local Channel Attention for Object Detection
Engineering Applications of Artificial Intelligence, 2023Rongsheng Lu, Dahang Wan, Siyuan Shen
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Channel-Wise Attention and Channel Combination for Knowledge Distillation
Proceedings of the International Conference on Research in Adaptive and Convergent Systems, 2020Knowledge distillation is a strategy to build machine learning models efficiently by making use of knowledge embedded in a pretrained model. Teacher-student framework is a well-known one to use knowledge distillation, where a teacher network usually contains knowledge for a specific task and a student network is constructed in a simpler architecture ...
Chan Sik Han, Keon Myung Lee
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019
Multi-band images beyond RGB are becoming popular in both commercial applications and research datasets, yet existing deep learning models were designed for academic RGB datasets. In this talk, we propose Channel Attention Networks (CAN), a deep learning model that uses soft attention on individual channels.
Alexei Bastidas, Hanlin Tang
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Multi-band images beyond RGB are becoming popular in both commercial applications and research datasets, yet existing deep learning models were designed for academic RGB datasets. In this talk, we propose Channel Attention Networks (CAN), a deep learning model that uses soft attention on individual channels.
Alexei Bastidas, Hanlin Tang
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Image super-resolution via channel attention and spatial attention
Applied Intelligence, 2021Deep convolutional networks have been widely applied in super-resolution (SR) tasks and have achieved excellent performance. However, even though the self-attention mechanism is a hot topic, has not been applied in SR tasks. In this paper, we propose a new attention-based network for more flexible and efficient performance than other generative ...
Enmin Lu, Xiaoxiao Hu
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Cell Counting with Channels Attention
2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP), 2020The low-quality images and the occlusions impede the accuracy of cell counting. Many networks focus on enlarging the receptive fields or expanding the single network to a multi-resolution network to enhance the counting accuracy. However, few networks are interested in channel adjustment. In this paper, we propose a weighted channel module to emphasize
Ni Jiang, Feihong Yu
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A K + Channel Worthy of Attention
Science, 1996A new family of K + channels has been defined in a paper in this week's issue [see Köhler et al . ( p. 1709 )], reporting the cloning of three members. The Perspective by Hille explains why this class of channels may underlie the control of attention in the brain by ...
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