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检索条件"主题词=Few-Shot Object Detection"
127 条 记 录,以下是41-50 订阅
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few-shot object detection with Refined Contrastive Learning  35
Few-shot Object Detection with Refined Contrastive Learning
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35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
作者: Shangguan, Zeyu Huai, Lian Liu, Tong Jiang, Xingqun BOE Technol Grp Co Ltd AIoT CTO Beijing Peoples R China
Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are sti... 详细信息
来源: 评论
A comprehensive review of few-shot object detection on aerial imagery
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Computer Science Review 2025年 57卷
作者: Khang Nguyen Nhat-Thanh Huynh Duc-Thanh Le Dien-Thuc Huynh Thi-Thanh-Trang Bui Truong Dinh Khanh-Duy Nguyen Tam V. Nguyen University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Dayton OH United States
With the development of technology, drones, and satellites play an important role in human life. Related research problems receive great attention, especially in the computer vision community. Notably, the object dete... 详细信息
来源: 评论
Semi-Supervised few-shot object detection via Adaptive Pseudo Labeling
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年 第4期34卷 2151-2165页
作者: Tang, Yingbo Cao, Zhiqiang Yang, Yuequan Liu, Jierui Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou 225127 Peoples R China Yangzhou Univ Coll Artificial Intelligence Yangzhou 225127 Peoples R China Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESAT Beijing 100871 Peoples R China
few-shot object detection (FSOD) aims to detect novel objects with limited annotated examples. Mainstream methods suffer from the data scarcity of novel classes with insufficient intra-class variations, which makes th... 详细信息
来源: 评论
Boosting few-shot object detection with Discriminative Representation and Class Margin
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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年 第3期20卷 1-19页
作者: Shi, Yanyan Yang, Shaowu Yang, Wenjing Shi, Dianxi Li, Xuehui Natl Univ Def Technol Coll Comp Changsha Hunan Peoples R China Natl Innovat Inst Def Technol Changsha Peoples R China
Classifying and accurately locating a visual category with few annotated training samples in computer vision has motivated the few-shot object detection technique, which exploits transfering the source-domain detectio... 详细信息
来源: 评论
ECEA: Extensible Co-Existing Attention for few-shot object detection
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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 5564-5576页
作者: Xin, Zhimeng Wu, Tianxu Chen, Shiming Zou, Yixiong Shao, Ling You, Xinge Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Univ Chinese Acad Sci UCAS UCAS Terminus AI Lab Beijing 100101 Peoples R China
few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundan... 详细信息
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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
来源: 评论
SD-FSOD: Self-Distillation Paradigm via Distribution Calibration for few-shot object detection
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年 第7期34卷 5963-5976页
作者: Chen, Han Wang, Qi Xie, Kailin Lei, Liang Lin, Matthieu Gaetan Lv, Tian Liu, Yongjin Luo, Jiebo Guangdong Univ Technol Guangzhou 510006 Peoples R China Guizhou Univ Coll Comp Sci & Technol State Key Lab Publ Big Data Guiyang 550025 Guizhou Peoples R China Tsinghua Univ Coll Comp Sci & Technol Beijing 100084 Peoples R China Univ Rochester Dept Comp Sci Rochester NY 14627 USA
few-shot object detection (FSOD) aims to detect novel targets with only a few instances of the associated samples. Although combinations of distillation techniques and meta-learning paradigms have been acknowledged as... 详细信息
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Extreme R-CNN: few-shot object detection via Sample Synthesis and Knowledge Distillation
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SENSORS 2024年 第23期24卷 7833页
作者: Zhang, Shenyong Wang, Wenmin Wang, Zhibing Li, Honglei Li, Ruochen Zhang, Shixiong Macau Univ Sci & Technol Sch Comp Sci & Engn Macau 999078 Peoples R China Beijing Inst Technol Sch Comp Technol Zhuhai 519088 Peoples R China Chongqing Polytech Univ Elect Technol Artificial Intelligence & Big Data Coll Chongqing 401331 Peoples R China Guangdong BOHUA UHD Video Innovat Ctr Co Ltd Shenzhen 518172 Peoples R China
Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biase... 详细信息
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Understanding Negative Proposals in Generic few-shot object detection
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年 第7期34卷 5818-5829页
作者: Yan, Bowei Lang, Chunbo Cheng, Gong Han, Junwei Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China
Recently, few-shot object detection (FSOD) has received considerable research attention as a strategy for reducing reliance on extensively labeled bounding boxes. However, current approaches encounter significant chal... 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