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检索条件"主题词=Few-Shot Object Detection"
128 条 记 录,以下是41-50 订阅
排序:
SAPT: Saliency Augmentation and Unsupervised Pre-trained Model Fusion for few-shot object detection
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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2024年 第11期96卷 747-762页
作者: Liao, Yujun Wu, Yan Mo, Yujian He, Yufei Hu, Yinghao Zhao, Junqiao Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Intelligent Vehicle Shanghai 201804 Peoples R China
object detection algorithms require a large amount of annotated data for training and optimization, which can be time-consuming, expensive, and limit model robustness and generalization. The natural world has a divers... 详细信息
来源: 评论
A Neuroinspired Contrast Mechanism enables few-shot object detection
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PATTERN RECOGNITION 2024年 156卷
作者: Yang, Lingxiao Chen, Dapeng Chen, Yifei Peng, Wei Xie, Xiaohua Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China Huawei Technol Shenzhen Peoples R China
Deep learning-based object detectors often demand abundant annotated data for training. However, in practice, only limited training data are available, making few-shot object detection (FSOD) an attractive research to... 详细信息
来源: 评论
FSODv2: A Deep Calibrated few-shot object detection Network
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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年 第9期132卷 3566-3585页
作者: Fan, Qi Zhuo, Wei Tang, Chi-Keung Tai, Yu-Wing Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Intelligence Sci & Technol Suzhou Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China Dartmouth Coll Comp Sci Dept Hanover NH 03755 USA
Traditional methods for object detection typically necessitate a substantial amount of training data, and creating high-quality training data is time-consuming. We propose a novel few-shot object detection network (FS... 详细信息
来源: 评论
Low-light few-shot object detection via curve contrast enhancement and flow-encoder-based variational autoencoder
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Neural Computing and Applications 2025年 第10期37卷 7261-7278页
作者: Jiang, Zetao Jin, Xin Kang, Junjie The Key Laboratory of image and graphic intelligent processing Guilin University of Electronic Technology Guangxi Guilin541004 China Wuhan Service Department Sinopec Shared Services.LTD.Nanjing Branch Jiangsu Nanjing210018 China Co. LTD Geely Group Zhejiang Ningbo315336 China
Aiming at the problem of insufficient samples in low-light object detection in some environments, a low-light few-shot object detection method based on curve contrast enhancement and flow-encoder-based variational aut... 详细信息
来源: 评论
Text generation and multi-modal knowledge transfer for few-shot object detection
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PATTERN RECOGNITION 2025年 161卷
作者: Du, Yaoyang Liu, Fang Jiao, Licheng Li, Shuo Hao, Zehua Li, Pengfang Wang, Jiahao Wang, Hao Liu, Xu Minist Educ Key Lab Intelligent Percept & Image Understanding Beijing Peoples R China Int Res Ctr Intelligent Percept & Computat Beijing Peoples R China Joint Int Res Lab Intelligent Percept & Computat Beijing Peoples R China Xidian Univ Sch Artificial Intelligent Xian 710071 Shaanxi Peoples R China
The challenge of detecting novel categories with limited annotated samples for learning is referred to as few shot object detection (FSOD). Due to the scarcity of data, networks struggle to learn robust features that ... 详细信息
来源: 评论
Multiple knowledge embedding for few-shot object detection
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SIGNAL IMAGE AND VIDEO PROCESSING 2023年 第5期17卷 2231-2240页
作者: Gong, Xiaolin Cai, Youpeng Wang, Jian Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Imaging & Sensing Microelect Techn Tianjin 300072 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China
In the problem of few-shot object detection, class prototype knowledge in previous works is not be fully refined and utilized due to lack of instances. We noticed that the application of the output features of the RoI... 详细信息
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A broader study of cross-domain few-shot object detection
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APPLIED INTELLIGENCE 2023年 第23期53卷 29465-29485页
作者: Sa, Liangbing Yu, Chongchong Hong, Zhaorui Zheng, Tong Liu, Sihan Beijing Technol & Business Univ Beijing 100048 Peoples R China
Latest studies on few-shot object detection (FSOD) mainly focuses on achieving better performance in novel class through few-shot fine-tuning. This approach is based on the large amount of annotated data in base class... 详细信息
来源: 评论
RecFRCN: few-shot object detection With Recalibrated Faster R-CNN
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IEEE ACCESS 2023年 11卷 121109-121117页
作者: Zhang, Youyou Lu, Tongwei Wuhan Inst Technol Sch Comp Sci & Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China
Currently, Faster R-CNN serves as the fundamental detection framework in the majority of few-shot object detection algorithms. However, due to limited samples per class, the Faster R-CNN's classification branch fa... 详细信息
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Efficient few-shot object detection via Knowledge Inheritance
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 321-334页
作者: Yang, Ze Zhang, Chi Li, Ruibo Xu, Yi Lin, Guosheng Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore 639798 Singapore OPPO US Res Ctr Inno Peak Technol Inc Palo Alto CA 94303 USA
few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ig... 详细信息
来源: 评论
few-shot object detection in Remote Sensing Images via Data Clearing and Stationary Meta-Learning
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SENSORS 2024年 第12期24卷 3882页
作者: Yang, Zijiu Guan, Wenbin Xiao, Luyang Chen, Honggang Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China
Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing images (RSIs) and FSOD, we propose a meta-learning-b... 详细信息
来源: 评论