Suchergebnisse - "Cascade encoder–decoder"

Image-Based Flood Disaster Detection in Solar Greenhouses Using a Residual Multiscale Cascade Encoder–Decoder Network (RMCED)​

Autoren: Juntao Zhao Tiangang Lu Xinyue Lv et al.

CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction

Autoren: Zhang, Gang Li, Ziyi Tang, Chufeng et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2302.06052

CEDNet: A cascade encoder–decoder network for dense prediction

Autoren: Gang Zhang Ziyi Li Chufeng Tang et al.

Quelle: Pattern Recognition. 158:111072

Schlagwörter: FOS: Computer and information sciences, 03 medical and health sciences, 0302 clinical medicine, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://arxiv.org/abs/2302.06052

Cednet: A Cascade Encoder-Decoder Network for Dense Prediction

Autoren: Zhang, Gang Li, Ziyi Tang, Chufeng et al.

Verfügbarkeit: https://doi.org/10.2139/ssrn.4857945

Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning.

Autoren: Hu, Yilei Xu, Jinyang Gou, Zhichao et al.

Quelle: Artificial Intelligence in Agriculture; Dec2025, Vol. 15 Issue 4, p816-828, 13p

Schlagwörter: RECOGNITION (Psychology), CHICKENS, CASCADE connections, POULTRY farms, COMPUTER vision

Multi-Scale Kolmogorov-Arnold Network (KAN)-Based Linear Attention Network: Multi-Scale Feature Fusion with KAN and Deformable Convolution for Urban Scene Image Semantic Segmentation.

Autoren: Li, Yuanhang Liu, Shuo Wu, Jie et al.

Quelle: Remote Sensing; Mar2025, Vol. 17 Issue 5, p802-1, 22p

Schlagwörter: REMOTE sensing, FEATURE extraction

LocSAR: a location-based SAR ship generation model for complex backgrounds.

Autoren: Ju, Moran Li, Mulin Mao, Tengkai et al.

Quelle: Signal, Image & Video Processing; Jan2025, Vol. 19 Issue 1, p1-11, 11p

A Dual-Modality T-UNet for Automatic Segmentation of Key Thermal Regions in Cattle.

Autoren: Hu, Bingnan Yang, Xuanjiang Li, Hualong et al.

Quelle: Journal of Physics: Conference Series; 2025, Vol. 3135 Issue 1, p1-11, 11p

Recognizing and localizing chicken behaviors in videos based on spatiotemporal feature learning

Autoren: Yilei Hu Jinyang Xu Zhichao Gou et al.

Quelle: Artificial Intelligence in Agriculture, Vol 15, Iss 4, Pp 816-828 (2025)

Schlagwörter: Behavior localization, Behavior recognition, Chicken, Computer vision, Spatiotemporal feature, Agriculture

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2589721725000698; https://doaj.org/toc/2589-7217

Zugangs-URL: https://doaj.org/article/b2047ec6415b45ac85b688b0c0a2edc9

PWStableNet: Learning Pixel-Wise Warping Maps for Video Stabilization.

Autoren: Zhao, Minda Ling, Qiang

Quelle: IEEE Transactions on Image Processing; 2020, Vol. 29, p3582-3595, 14p

Schlagwörter: PIXELS, DEEP learning, VIDEOS, CASCADE connections, PARALLAX

Towards a machine-learning-based large eddy simulation of offshore wind farms.

Autoren: Marefat, H. Ali Alam, Jahrul Pope, Kevin

Quelle: Computers & Fluids. Nov2025, Vol. 302, pN.PAG-N.PAG. 1p.

Schlagwörter: *MACHINE learning, *LARGE eddy simulation models, *TURBULENCE, *ENERGY transfer, *OFFSHORE wind power plants, *AUTOENCODERS, *DYNAMIC models

Multi-dimensional perception-guided iterative reflection removal network with deep features for painting images.

Autoren: Xie, Yuqi Zhang, Xiaojuan Zhao, Yang

Quelle: Scientific Reports; 9/29/2025, Vol. 15 Issue 1, p1-16, 16p

Schlagwörter: IMAGE quality analysis, IMAGE processing, IMAGE enhancement (Imaging systems), ARTIFICIAL neural networks, INPAINTING

CSTC: Visual Transformer Network with Multimodal Dual Fusion for Hyperspectral and LiDAR Image Classification.

Autoren: Mei, Yong Fan, Jinlong Fan, Xiangsuo et al.

Quelle: Remote Sensing; Sep2025, Vol. 17 Issue 18, p3158, 25p

Schlagwörter: REMOTE sensing, HYPERSPECTRAL imaging systems, FEATURE extraction, TRANSFORMER models, REMOTE-sensing images, DEEP learning, MULTIMODAL user interfaces

ADAC: an active domain adaptive network with progressive learning strategy for cloud detection of remote sensing imagery.

Autoren: Wenxin, Wang Xu, Kai Anling, Wang et al.

Quelle: Geo-Spatial Information Science; Aug2025, Vol. 28 Issue 4, p1597-1614, 18p

Schlagwörter: REMOTE sensing, ACTIVE learning, TRANSFER of training, CONVOLUTIONAL neural networks, TRANSFORMER models, LANDSAT satellites, IMAGE processing

Video Stabilization Algorithm Based on View Boundary Synthesis.

Autoren: Shan, Wenchao Zhao, Hejing Li, Xin et al.

Quelle: Symmetry (20738994); Aug2025, Vol. 17 Issue 8, p1351, 21p

Schlagwörter: IMAGE stabilization, OPTICAL flow, SYMMETRY, VIDEO processing

Drought Stress Grading Model for Apple Rootstock Softwood Cuttings Based on the CU-ICA-Net.

Autoren: Wang, Xu Wang, Pengfei Li, Jianping et al.

Quelle: Agronomy; Jul2025, Vol. 15 Issue 7, p1508, 23p

Schlagwörter: COMPUTER vision, PLANT cuttings, AUTOMATIC control systems, ROOTSTOCKS, MACHINE learning, DROUGHTS, HYDRATION, ARTIFICIAL neural networks

Robust visual tracking using temporal regularization correlation filter with high-confidence strategy.

Autoren: Xiao-Gang Dong Ke-Xuan Li Hong-Xia Mao et al.

Quelle: Journal of Electronic Science & Technology; Jun2025, Vol. 23 Issue 2, p1-14, 14p

FI2Net: a two-stage network with information fusion and integration mechanisms for surface defect detection.

Autoren: Wang, ZhiPeng Song, YongHong

Quelle: Measurement Science & Technology; Jun2025, Vol. 36 Issue 6, p1-13, 13p

Schlagwörter: SURFACE defects, INDUSTRIAL goods, INFORMATION networks, INFORMATION design, ATTENTION

Swim-Rep fusion net: A new backbone with Faster Recurrent Criss Cross Polarized Attention.

Autoren: Li, Zhe

Quelle: PLoS ONE; 5/27/2025, Vol. 20 Issue 5, p1-12, 12p

Schlagwörter: IMAGE recognition (Computer vision), DATABASES, REMOTE sensing, SPINE, CLASSIFICATION, DEEP learning

用于三维点云分割和分类的高分辨率特征网络. (Chinese)

Alternate Title: High-resolution Feature Network for 3D Point Cloud Segmentation and Classification. (English)

Autoren: 朱骏杰 刘东峰

Quelle: Journal of Guangdong University of Technology; May2025, Vol. 42 Issue 3, p44-51, 8p

Schlagwörter: POINT cloud, POINT processes, LEARNING modules, SPINE, CLASSIFICATION