Results 221 to 230 of about 290,361 (261)
Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks. [PDF]
Sensors (Basel)Wu J +5 more
europepmc +1 more source
Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification. [PDF]
Sci RepZhao Y, Sun J, Hu N, Zai C, Han Y.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention
IEEE Transactions on Affective Computing, 2023Emotion recognition based on electroencephalography (EEG) is a significant task in the brain-computer interface field. Recently, many deep learning-based emotion recognition methods are demonstrated to outperform traditional methods. However, it remains challenging to extract discriminative features for EEG emotion recognition, and most methods ignore ...
Wei Tao +6 more
openaire +1 more source
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
openaire +2 more sources
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 ...
openaire +2 more sources