Results 221 to 230 of about 304,469 (259)
Some of the next articles are maybe not open access.
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
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 Bastidas, Hanlin Tang
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
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
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
IPTV Channel Zapping Recommendation With Attention Mechanism
IEEE Transactions on Multimedia, 2021Internet Protocol TV (IPTV) normally has the advantage of providing far more TV channels than the traditional TV services, while as the other side of the coin it has the problem of information overload. Users of IPTV usually have difficulties finding channels matching their interests.
Guangyu Li +5 more
openaire +1 more source
NonLocal Channel Attention for NonHomogeneous Image Dehazing
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020The emergence of deep learning methods that complement traditional model-based methods has helped achieve a new state-of-the-art for image dehazing. Many recent methods design deep networks that either estimate the haze-free image (J) directly or estimate physical parameters in the haze model, i.e. ambient light (A) and transmission map (t) followed by
Kareem M. Metwaly +3 more
openaire +1 more source
DepthWise Attention: Towards Individual Channels Attention
2023 IEEE Symposium on Computers and Communications (ISCC), 2023Zhilei Zhu +3 more
openaire +1 more source
Partial channel pooling attention beats convolutional attention
Expert Systems with Applications, 2023Jun Zhang 0113, Wushour Slamu
openaire +1 more source
Scale channel attention network for image segmentation
Multimedia Tools and Applications, 2020The object scale variation results in a negative effect on image segmentation performance. Spatial pyramid pooling module or the attention mechanism are two widely used components in deep neural networks to handle this problem. Applying the single component commonly achieves limited benefit.
Jianjun Chen +4 more
openaire +1 more source
Learning Spatial-Channel Attention for Visual Tracking
2019 IEEE/CIC International Conference on Communications in China (ICCC), 2019Convolutional neural networks have an advantage of strong representation and has been widely applied to visual tracking. However, simply deepening network in pursuit of boosting performance is inappropriate for tracking because of its speed requirement.
Yingsen Zeng, Haiying Wang 0005, Ting Lu
openaire +1 more source