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Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases.
Ye Huang +4 more
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Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases.
Ye Huang +4 more
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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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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 A. 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 A. Bastidas, Hanlin Tang
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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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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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Global channel attention networks for intracranial vessel segmentation
Computers in Biology and Medicine, 2020Intracranial blood vessel segmentation plays an essential role in the diagnosis and surgical planning of cerebrovascular diseases. Recently, deep convolutional neural networks have shown increasingly outstanding performance in image classification and also in the field of image segmentation.
Jiajia, Ni +6 more
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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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Partial channel pooling attention beats convolutional attention
Expert Systems with Applications, 2023Jun Zhang, Wushour Slamu
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DepthWise Attention: Towards Individual Channels Attention
2023 IEEE Symposium on Computers and Communications (ISCC), 2023Zhilei Zhu +3 more
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PointNet-Based Channel Attention VLAD Network
2019With the upgrading of application scenarios, computer vision is progressively expanded to 3D. Many methods that process point cloud directly provide a new paradigm for 3D understanding. Most of these methods employ maxpooling to handle the sparsity and disorder of point cloud.
Rongrong Fan, Hui Shuai, Qingshan Liu
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