CSpace

浏览/检索结果: 共10条,第1-10条 帮助

已选(0)清除 条数/页:   排序方式:
Hierarchical compositional representations for few-shot action recognition 期刊论文
COMPUTER VISION AND IMAGE UNDERSTANDING, 2024, 卷号: 240, 页码: 11
作者:  Li, Changzhen;  Zhang, Jie;  Wu, Shuzhe;  Jin, Xin;  Shan, Shiguang
收藏  |  浏览/下载:14/0  |  提交时间:2024/05/20
Action recognition  Few-shot learning  Hierarchical compositional representations  Body parts  EMD distance  
Improving metric-based few-shot learning with dynamically scaled softmax loss 期刊论文
IMAGE AND VISION COMPUTING, 2023, 卷号: 140, 页码: 15
作者:  Zhang, Yu;  Zuo, Xin;  Zheng, Xuxu;  Gao, Xiaoyong;  Wang, Bo;  Hu, Weiming
收藏  |  浏览/下载:7/0  |  提交时间:2024/05/20
Few-shot learning  Metric-based learning framework  Softmax loss improvement  
Reference-Based Deep Line Art Video Colorization 期刊论文
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2023, 卷号: 29, 期号: 6, 页码: 2965-2979
作者:  Shi, Min;  Zhang, Jia-Qi;  Chen, Shu-Yu;  Gao, Lin;  Lai, Yu-Kun;  Zhang, Fang-Lue
收藏  |  浏览/下载:18/0  |  提交时间:2023/12/04
Image color analysis  Art  Animation  Feature extraction  Three-dimensional displays  Transforms  Color  Line art colorization  color transform  temporal coherence  few shot learning  
Composite Object Relation Modeling for Few-Shot Scene Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 5678-5691
作者:  Song, Xinhang;  Liu, Chenlong;  Zeng, Haitao;  Zhu, Yaohui;  Chen, Gongwei;  Qin, Xiaorong;  Jiang, Shuqiang
收藏  |  浏览/下载:13/0  |  提交时间:2023/12/04
Scene recognition  few-shot learning  graph modeling  generalization ability  
Dataset Bias in Few-Shot Image Recognition 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 1, 页码: 229-246
作者:  Jiang, Shuqiang;  Zhu, Yaohui;  Liu, Chenlong;  Song, Xinhang;  Li, Xiangyang;  Min, Weiqing
收藏  |  浏览/下载:18/0  |  提交时间:2023/07/12
Dataset bias  few-shot image recognition  knowledge transfer  meta-learning  
Re-FeMAT: A Reconfigurable Multifunctional FeFET-Based Memory Architecture 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 卷号: 41, 期号: 11, 页码: 5071-5084
作者:  Zhang, Xiaoyu;  Liu, Rui;  Song, Tao;  Yang, Yuxin;  Han, Yinhe;  Chen, Xiaoming
收藏  |  浏览/下载:18/0  |  提交时间:2023/07/12
Convolutional neural network (CNN)  ferroelectric field-effect transistor (FeFET)  few-shot learning  in-memory processing  ternary content-addressable memory (TCAM)  
Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 8, 页码: 3458-3470
作者:  Lai, Nan;  Kan, Meina;  Han, Chunrui;  Song, Xingguang;  Shan, Shiguang
收藏  |  浏览/下载:41/0  |  提交时间:2021/12/01
Task analysis  Adaptation models  Training  Predictive models  Feature extraction  Generators  Computational modeling  Few-shot learning  meta-learning  predict classifier weights  task-adaptive predictor  
Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation 期刊论文
MEDICAL PHYSICS, 2021, 页码: 14
作者:  Ma, Jun;  Wang, Yixin;  An, Xingle;  Ge, Cheng;  Yu, Ziqi;  Chen, Jianan;  Zhu, Qiongjie;  Dong, Guoqiang;  He, Jian;  He, Zhiqiang;  Cao, Tianjia;  Zhu, Yuntao;  Nie, Ziwei;  Yang, Xiaoping
收藏  |  浏览/下载:49/0  |  提交时间:2021/12/01
COVID‐  19 CT  domain generalization  few‐  shot learning  knowledge transfer  lung and infection segmentation  
Attribute-Guided Feature Learning for Few-Shot Image Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 1200-1209
作者:  Zhu, Yaohui;  Min, Weiqing;  Jiang, Shuqiang
收藏  |  浏览/下载:33/0  |  提交时间:2021/12/01
Image recognition  Training  Task analysis  Semantics  Standards  Measurement  Visualization  Attribute learning  few-shot learning  image recognition  
Few-shot Food Recognition via Multi-view Representation Learning 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 3, 页码: 20
作者:  Jiang, Shuqiang;  Min, Weiqing;  Lyu, Yongqiang;  Liu, Linhu
收藏  |  浏览/下载:56/0  |  提交时间:2020/12/10
Food recognition  few-shot learning  visual recognition  deep learning