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检索条件"机构=Pattern Recognition and Image Processing"
1790 条 记 录,以下是191-200 订阅
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Mushroom Classification Based on Deep Residual Network
Mushroom Classification Based on Deep Residual Network
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pattern recognition and Machine Learning (PRML), IEEE International Conference on
作者: Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China
Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classificatio...
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FastFlowNet: A lightweight network for fast optical flow estimation
arXiv
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arXiv 2021年
作者: Kong, Lingtong Shen, Chunhua Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The University of Adelaide Australia
Dense optical flow estimation plays a key role in many robotic vision tasks. It has been predicted with satisfying accuracy than traditional methods with advent of deep learning. However, current networks often occupy... 详细信息
来源: 评论
Noise Perturbation Based Graph Contrastive Learning via Flexible Filters for Node Classification
Noise Perturbation Based Graph Contrastive Learning via Flex...
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International Joint Conference on Neural Networks (IJCNN)
作者: Zhilong Xiong Jia Cai Ranhui Yan Xiaolin Huang xFusion Digital Technologies Company Limited Shenzhen China School of Digital Economics Guangdong University of Finance & Economics Guangzhou China School of Statistics and Mathematics Guangdong University of Finance & Economics Guangzhou China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China
Graph neural networks (GNNs), as a powerful deep learning framework for modeling graph-structured data, have attracted lots of attention recently. Most of existing GNNs need a lot of labeled data. However, constructin... 详细信息
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FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
FastFlowNet: A Lightweight Network for Fast Optical Flow Est...
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IEEE International Conference on Robotics and Automation (ICRA)
作者: Lingtong Kong Chunhua Shen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The University of Adelaide Australia
Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current netwo... 详细信息
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Object Detector based on Enhanced Multi-scale Feature Fusion Pyramid Network
Object Detector based on Enhanced Multi-scale Feature Fusion...
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IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
作者: Luan Zhao Xiaofeng Zhang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China
Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and dete... 详细信息
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SiamORPN: Enabling Orthogonality between Object and Background in Siamese Object Tracking
SiamORPN: Enabling Orthogonality between Object and Backgrou...
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International Conference on Tools for Artificial Intelligence (ICTAI)
作者: Kai Huang Chaolin Pan Jun Chu Lu Leng Jun Miao Junjiang Wu Lingfeng Wang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Science and Technology Beijing University of Chemical Technology Beijing China
Siamese-based trackers currently are the dominant tracking paradigm due to the balance between speed and performance. However, it is prone to drift and tracking failure when the environment is complex and similar obje... 详细信息
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Consistency-Guided Adaptive Alternating Training for Semi-Supervised Salient Object Detection
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IEEE Transactions on Circuits and Systems for Video Technology 2025年
作者: Chen, Liyuan Liu, Wei Wang, Hua Jeon, Sang-Woon Jiang, Yunliang Zheng, Zhonglong Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Victoria University Institute for Sustainable Industries and Liveable Cities College of Engineering and Science MelbourneVIC8001 Australia Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of
This paper presents a novel approach that leverages two models to integrate features from numerous unlabeled images, addressing the challenge of semi-supervised salient object detection (SSOD). Unlike conventional met... 详细信息
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Hybrid Data-Free Knowledge Distillation
arXiv
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arXiv 2024年
作者: Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generati... 详细信息
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Unsupervised Difference Learning for Noisy Rigid image Alignment
arXiv
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arXiv 2022年
作者: Chen, Yu-Xuan Feng, Dagan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia
Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spat... 详细信息
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Application of an Improved Focal Loss in Vehicle Detection  19th
Application of an Improved Focal Loss in Vehicle Detection
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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020
作者: He, Xuanlin Yang, Jie Kasabov, Nikola Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Auckland University of Technology Auckland New Zealand
Object detection is an important and fundamental task in computer vision. Recently, the emergence of deep neural network has made considerable progress in object detection. Deep neural network object detectors can be ... 详细信息
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