activity
20182022
most citedAttentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

47 citations · 90 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV202247 cited

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

Yan Zhao, Lingjun Zhao, Zhong Liu +3

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearan…

cs.CV202114 cited

Pixel Difference Networks for Efficient Edge Detection

Zhuo Su, Wenzhe Liu, Zitong Yu +5

Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the hi…

cs.CV202122 cited

Deep Learning for Scene Classification: A Survey

Delu Zeng, Minyu Liao, Mohammad Tavakolian +5

Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challengin…

cs.CV2020

FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond

Zhuo Su, Linpu Fang, Deke Guo +3

Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated…

cs.CV20204 cited

Dynamic Group Convolution for Accelerating Convolutional Neural Networks

Zhuo Su, Linpu Fang, Wenxiong Kang +3

Replacing normal convolutions with group convolutions can significantly increase the computational efficiency of modern deep convolutional networks, which has been widely adopted i…

cs.CV2019

Deep Learning for 3D Point Clouds: A Survey

Yulan Guo, Hanyun Wang, Qingyong Hu +3

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominatin…