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Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks 期刊论文
IEEE Transactions on Intelligent Transportation Systems, 2021, 卷号: /, 期号: /, 页码: /
作者:  Zhang, Yujia;  Wu, Junxian;  Li, Qianzhong;  Zhao, Xiaoguang;  Tan, Min
Adobe PDF(12585Kb)  |  收藏  |  浏览/下载：338/56  |  提交时间：2022/04/02
Fine-grained defect segmentation  Crack detection  Semantic segmentation  Pavement inspection  
Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA 期刊论文
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2021, 期号: 2021.5, 页码: 1-1
作者:  Li, Gang;  Liu, Zejian;  Li, Fanrong;  Cheng, Jian
Adobe PDF(6174Kb)  |  收藏  |  浏览/下载：189/38  |  提交时间：2022/02/15
block convolution  memory-efficient  off-chip transfer  fpga  cnn accelerator  
ECBC: Efficient Convolution via Blocked Columnizing 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:  Zhao, Tianli;  Hu, Qinghao;  He, Xiangyu;  Xu, Weixiang;  Wang, Jiaxing;  Leng, Cong;  Cheng, Jian
Adobe PDF(3003Kb)  |  收藏  |  浏览/下载：299/33  |  提交时间：2022/01/27
Convolution  Tensors  Layout  Memory management  Indexes  Transforms  Performance evaluation  Convolutional neural networks (CNNs)  direct convolution  high performance computing for mobile devices  im2col convolution  memory-efficient convolution (MEC)  
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 2, 页码: 661-673
作者:  Gao, Naiyu;  Shan, Yanhu;  Wang, Yupei;  Zhao, Xin;  Huang, Kaiqi
Adobe PDF(4190Kb)  |  收藏  |  浏览/下载：295/48  |  提交时间：2021/03/29
Instance segmentation  panoptic segmentation  pixel-pair affinity  graph partition  
Robot learning through observation via coarse-to-fine grained video summarization 期刊论文
APPLIED SOFT COMPUTING, 2021, 卷号: 99, 期号: /, 页码: 106913
作者:  Zhang, Yujia;  Li, Qianzhong;  Zhao, Xiaoguang;  Tan, Min
Adobe PDF(5989Kb)  |  收藏  |  浏览/下载：358/74  |  提交时间：2021/03/08
Robotic vision  Learning through observation  Coarse-to-fine video summarization