Search Results for author: Qing Zhu

Found 16 papers, 9 papers with code

Reviewing continual learning from the perspective of human-level intelligence

no code implementations23 Nov 2021 Yifan Chang, Wenbo Li, Jian Peng, Bo Tang, Yu Kang, Yinjie Lei, Yuanmiao Gui, Qing Zhu, Yu Liu, Haifeng Li

Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism.

Continual Learning

Curvature Graph Neural Network

no code implementations30 Jun 2021 Haifeng Li, Jun Cao, Jiawei Zhu, Yu Liu, Qing Zhu, Guohua Wu

And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.

Node Classification

Graph Information Vanishing Phenomenon inImplicit Graph Neural Networks

no code implementations2 Mar 2021 Haifeng Li, Jun Cao, Jiawei Zhu, Qing Zhu, Guohua Wu

A class of GNNs solves this problem by learning implicit weights to represent the importance of neighbor nodes, which we call implicit GNNs such as Graph Attention Network.

Graph Attention

Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

no code implementations26 Nov 2020 Qing Zhu, Shengzhi Huang, Han Hu, Haifeng Li, Min Chen, Ruofei Zhong

Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings.

Structure-Aware Completion of Photogrammetric Meshes in Urban Road Environment

1 code implementation23 Nov 2020 Qing Zhu, Qisen Shang, Han Hu, Haojia Yu, Ruofei Zhong

Finally, the completed rendered image is deintegrated to the original texture atlas and the triangles for the vehicles are also flattened for improved meshes.

Object Detection

Minimum Potential Energy of Point Cloud for Robust Global Registration

no code implementations11 Jun 2020 Zijie Wu, Yaonan Wang, Qing Zhu, Jianxu Mao, Haotian Wu, Mingtao Feng, Ajmal Mian

Different from the most existing point set registration methods which usually extract the descriptors to find correspondences between point sets, our proposed MPE alignment method is able to handle large scale raw data offset without depending on traditional descriptors extraction, whether for the local or global registration methods.

Deep Fusion of Local and Non-Local Features for Precision Landslide Recognition

1 code implementation20 Feb 2020 Qing Zhu, Lin Chen, Han Hu, Binzhi Xu, Yeting Zhang, Haifeng Li

The second uses a scale attention mechanism to guide the up-sampling of features from the coarse level by a learned weight map.

Semantic Segmentation

Fast and Regularized Reconstruction of Building Façades from Street-View Images using Binary Integer Programming

1 code implementation20 Feb 2020 Han Hu, Libin Wang, Mier Zhang, Yulin Ding, Qing Zhu

Regularized arrangement of primitives on building fa\c{c}ades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment.

3D Reconstruction

MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery

1 code implementation26 Oct 2019 Qing Zhu, Cheng Liao, Han Hu, Xiaoming Mei, Haifeng Li

This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries.

PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds

no code implementations20 Jun 2019 Jonathan Li, Rongren Wu, Yiping Chen, Qing Zhu, Zhipeng Luo, Cheng Wang

Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns.

Point Cloud Segmentation Semantic Segmentation

Overcoming Catastrophic Forgetting by Soft Parameter Pruning

1 code implementation4 Dec 2018 Jian Peng, Jiang Hao, Zhuo Li, Enqiang Guo, Xiaohong Wan, Deng Min, Qing Zhu, Haifeng Li

In this paper, we proposed a Soft Parameters Pruning (SPP) strategy to reach the trade-off between short-term and long-term profit of a learning model by freeing those parameters less contributing to remember former task domain knowledge to learn future tasks, and preserving memories about previous tasks via those parameters effectively encoding knowledge about tasks at the same time.

Continual Learning

Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

3 code implementations22 Oct 2018 Enqiang Guo, Xinsha Fu, Jiawei Zhu, Min Deng, Yu Liu, Qing Zhu, Haifeng Li

A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.

Scene Change Detection

Fast and Robust Matching for Multimodal Remote Sensing Image Registration

no code implementations19 Aug 2018 Yuanxin Ye, Lorenzo Bruzzone, Jie Shan, Francesca Bovolo, Qing Zhu

To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration.

Image Registration Template Matching

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