Results 21 to 30 of about 4,718,784 (295)

Attentional Sampling between Eye Channels

Journal of Cognitive Neuroscience, 2023
Abstract Our ability to detect targets in the environment fluctuates in time. When individuals focus attention on a single location, the ongoing temporal structure of performance fluctuates at 8 Hz. When task demands require the distribution of attention over two objects defined by their location, color or motion direction, ongoing ...
Daniele Re, Golan Karvat, Ayelet N. Landau   +2 more
openaire   +3 more sources

Channel Distillation: Channel-Wise Attention for Knowledge Distillation

CoRR, 2020
Knowledge distillation is to transfer the knowledge from the data learned by the teacher network to the student network, so that the student has the advantage of less parameters and less calculations, and the accuracy is close to the teacher. In this paper, we propose a new distillation method, which contains two transfer distillation strategies and a ...
Zaida Zhou, Chaoran Zhuge, Xinwei Guan, Wen Liu   +3 more
openaire   +3 more sources

Underwater object detection algorithm based on channel attention and feature fusion

Xibei Gongye Daxue Xuebao, 2022
Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection.
ZHANG Yan, LI Xingshan, SUN Yemei, LIU Shudong   +3 more
doaj   +1 more source

Channel attention module structure.

, 2022
Channel attention module structure.
Mengmeng Chen (565957), Mayire Ibrayim (13205749), Askar Hamdulla (13205752)   +2 more
core   +1 more source

A Novel Channel and Temporal-Wise Attention in Convolutional Networks for Multivariate Time Series Classification

IEEE Access, 2020
Multivariate time series classification (MTSC) is a fundamental and essential research problem in the domain of time series data mining. Recently deep neural networks emerged as an end-to-end solution for MTSC and achieve state-of-the-art results on ...
Xu Cheng, Peihua Han, Guoyuan Li, Shengyong Chen, Houxiang Zhang   +4 more
doaj   +1 more source

Self-Supervised Monocular Depth Estimation Based on Channel Attention

Photonics, 2022
Scene structure and local details are important factors in producing high-quality depth estimations so as to solve fuzzy artifacts in depth prediction results.
Bo Tao, Xinbo Chen, Xiliang Tong, Du Jiang, Baojia Chen   +4 more
doaj   +1 more source

Separable Attention Capsule Network for Signal Classification

IEEE Access, 2020
In this paper, a new Separable Attention Capsule Network (SACN) is proposed for signal classification. SACN is a light-weight network composed of multi-channel separable convolution layer, attention module and classification layer.
Shaoqing Liu, Huiling Liu, Chen Yang, Shuyuan Yang, Min Wang   +4 more
doaj   +1 more source

Multi-path channel attention and pixel attention modules.

, 2023
Multi-path channel attention and pixel attention modules.
Changjun Zou (1761103), Lintao Ye (6452534), Hangbin Xu (16827938)   +2 more
core   +1 more source

Multi-Scale Feature Channel Attention Generative Adversarial Network for Face Sketch Synthesis

IEEE Access, 2020
Face sketch synthesis for photos is an applied research topic and it is critical for criminal investigation. However, sketch synthesis remains some challenges because of the blur and artifacts in the generated face sketches. To mitigate these problems in
Jieying Zheng, Yahong Wu, Wanru Song, Ran Xu, Feng Liu   +4 more
doaj   +1 more source

Channel attention and spatial attention mechanism of CBAM.

, 2023
Channel attention and spatial attention mechanism of CBAM.
Qihang Li (8931125), Guohua Geng (14341340), Yi Jiang (40197), Zhan Li (402168), Long Feng (485547), Kang Li (107509)   +5 more
core   +1 more source