Mingyu Yang
14 Papers
Mingyu Yang is an academic researcher. The author has contributed to research in topics: Pattern recognition (psychology) & Environmental science. The author has an hindex of 1, co-authored 4 publications.
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Papers
Dual high-Q Fano resonances metasurfaces excited by asymmetric dielectric rods for refractive index sensing
Tianyu Wang,Siqi Liu,Jiahang Zhang,Liang Xu,Mingyu Yang,Ding Ma,Sijia Jiang,Qingbin Jiao,Xin Tan +8 more
TL;DR: High-Q Fano resonances metasurfaces excited by asymmetric dielectric rods for refractive index sensing enables dual-channel detection with high sensitivity and high Q-factor.
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Improved One-Stage Detectors with Neck Attention Block for Object Detection in Remote Sensing
TL;DR: Zhang et al. as mentioned in this paper designed a neck attention block (NAB), a simple and flexible module which combines the convolutional bottleneck structure and the attention mechanism, different from traditional attention mechanisms that focus on designing complicated attention branches.
Two-Level Spatio-Temporal Feature Fused Two-Stream Network for Micro-Expression Recognition
Zebiao Wang,Mingyu Yang,Qingbin Jiao,Liang Xu,Bing Han,Yuhang Li,Xin Tan +6 more
- 29 Feb 2024
TL;DR: Two-level spatio-temporal feature fused two-stream network for micro-expression recognition achieves improved performance by extracting richer features and reducing computation compared to traditional approaches.
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Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS
TL;DR: Wang et al. as discussed by the authors analyzed the evolutionary process and spatio-temporal association of land cover from 1990 to 2020, and the transfer matrix was used to analyze and reveal dynamic conversions.
A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n
Kaiqi Lang,Jie Cui,Mingyu Yang,Hanyu Wang,Zilong Wang,Honghai Shen +5 more
TL;DR: A novel Convolution with Transformer Attention Module (CTAM) is proposed to enhance object detection in remote sensing images by integrating local and global features. CTAM significantly improves the performance of YOLOv8n, achieving an impressive mAP of 54.2 at 50-95 IoU, surpassing YOLOv8n by a large margin.
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