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Saliency-guided meta-hallucinator for few-shot learning
《Science China(Information Sciences)》2024年第10期185-206,共22页Hongguang ZHANG Chun LIU Jiandong WANG Linru MA Piotr KONIUSZ Philip HSTORR Lin YANG 
supported by National Natural Science Foundation of China (Grant No. 62106282);Beijing Nova Program (Grant No. 20220484139)。
Learning novel object concepts from limited samples remains a considerable challenge in deep learning. The main directions for improving the few-shot learning models include(i) designing a stronger backbone,(ii) desig...
关键词：few-shot learning saliency detection object recognition anomaly detection computer vision 
RLLNet:a lightweight remaking learning network for saliency redetection on RGB-D images被引量：1
《Science China(Information Sciences)》2022年第6期75-76,共2页Wujie ZHOU Chang LIU Jingsheng LEI Lu YU 
supported by National Natural Science Foundation of China(Grant Nos.61502429,61972357)。
Dear editor,In recent years,given the importance of depth information for human vision and the popularity of depth sensing,visual salient object detection(SOD)from RGB-D(color and depth)images has attracted much atten...
关键词：EDITOR RGB VISUAL 
Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection被引量：2
《Science China(Information Sciences)》2022年第1期243-254,共12页Zengfu HOU Wei LI Ran TAO Pengge MA Weihua SHI 
supported by National Key Research and Development Project of China(Grant No.017YFB-0503903);National Natural Science Foundation of China(Grant Nos.61922013,61421001,U1833203);Key Scientific and Technological Projects in Henan Province(Grant No.192102210106);Beijing Natural Science Foundation(Grant No.L191004)。
Collaborative representation-based detection(CRD)has been developed in hyperspectral anomaly detection tasks and testified to be very effective;however,heterogeneous pixels in the background may affect the accuracy of...
关键词：HYPERSPECTRAL anomaly detection background purification collaborative representation saliency weight 
Capturing and tracking of building area based on structure saliency in airborne remote sensing video被引量：1
《Science China(Information Sciences)》2015年第4期172-174,共3页FENG Jing BI FuKun CHEN Liang ZHANG FeiFei CHEN He 
Object capturing and tracking in airborne remote sensing video is one of the hottest research topics, which is widely used in disaster monitoring and rescue and vehicle tracking. Because of the high real- time require...
关键词：Capturing and tracking of building area based on structure saliency in airborne remote sensing video SIFT area 
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