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Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation [article]

Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg
2020 arXiv   pre-print
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation.  ...  We further develop an adaptive class weighting loss to address the class imbalance.  ...  Multi-view SCG-Net (MSCG) for semantic labeling tasks with the proposed adaptive class weighting loss.  ... 
arXiv:2004.10327v1 fatcat:s7gtzdgudnclljcmqgrybdxqd4

Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation

Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Borre Salberg
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation.  ...  We further develop an adaptive class weighting loss to address the class imbalance.  ...  Multi-view SCG-Net (MSCG) for semantic labeling tasks with the proposed adaptive class weighting loss.  ... 
doi:10.1109/cvprw50498.2020.00030 dblp:conf/cvpr/LiuKJS20 fatcat:t3y4gdktzne3rninj2eqo6puou

SGA-Net: Self-Constructing Graph Attention Neural Network for Semantic Segmentation of Remote Sensing Images

Wenjie Zi, Wei Xiong, Hao Chen, Jun Li, Ning Jing
2021 Remote Sensing  
In this paper, a novel self-constructing graph attention neural network is proposed for such a purpose.  ...  Graph neural networks, which can capture global contextual representations, can exploit long-range pixel dependency, thereby improving semantic segmentation performance.  ...  In addition, we adopted an adaptive multi-class weighting (ACW) loss function [26] to address the highly imbalanced distribution of the classes.  ... 
doi:10.3390/rs13214201 fatcat:4wfmg6n3uraarlx3ccjogjr4ee

The 1st Agriculture-Vision Challenge: Methods and Results

Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier (+30 others)
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation  ...  Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation.  ...  Details of this work can be found in our workshop proceedings: Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation.  ... 
doi:10.1109/cvprw50498.2020.00032 dblp:conf/cvpr/ChiuXWHHHSWHSBD20 fatcat:rxgybproena7tpr3gusmmctqz4

Class-wise Dynamic Graph Convolution for Semantic Segmentation [article]

Hanzhe Hu, Deyi Ji, Weihao Gan, Shuai Bai, Wei Wu, Junjie Yan
2020 arXiv   pre-print
Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn  ...  Recent works have made great progress in semantic segmentation by exploiting contextual information in a local or global manner with dilated convolutions, pyramid pooling or self-attention mechanism.  ...  From one point of view, larger receptive field in deeper network is necessary for semantic prediction.  ... 
arXiv:2007.09690v1 fatcat:2nkmvl73dzcarnlp6kogcwvevu

Deep Learning for 3D Point Clouds: A Survey [article]

Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, Mohammed Bennamoun
2020 arXiv   pre-print
However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks.  ...  It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation.  ...  The inter-medium features are dynamically aggregated using a self-adapted receptive field and node weights. Liu et al.  ... 
arXiv:1912.12033v2 fatcat:qiiyvvuulfccxaiihf2mu23k34

The 1st Agriculture-Vision Challenge: Methods and Results [article]

Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier (+30 others)
2020 arXiv   pre-print
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation  ...  Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation.  ...  Details of this work can be found in our workshop proceedings: Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation. cross-entropy loss  ... 
arXiv:2004.09754v2 fatcat:mykchqunubd5hp73smyxap2zwy

Point cloud classification by dynamic graph CNN with adaptive feature fusion

Rui Guo, Yong Zhou, Jiaqi Zhao, Yiyun Man, Minjie Liu, Rui Yao, Bing Liu
2021 IET Computer Vision  
Based on the edge convolution and multi-layer perceptron, a local feature extractor is constructed.  ...  Our network mainly consists of three parts: global feature extractor, local feature extractor and adaptive feature fusion module.  ...  Multi-view Convolutional Neural Networks for 3D Shape Recognition (MVCNN) [24] collects a series of 2D images of the same 3D object from multi perspectives, Then, CNN is applied for each 2D image to  ... 
doi:10.1049/cvi2.12039 fatcat:onn22iai3rayvlcfqjh277po7i

Hierarchical Attention Networks for Medical Image Segmentation [article]

Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li
2019 arXiv   pre-print
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method  ...  The proposed HA module is robust to the variance of input and can be flexibly inserted into the existing convolution neural networks.  ...  And we propose a novel Hierarchical Attention Network (HANet) for medical image segmentation, which adaptively captures multi-level global context information in a high-order graph manner.  ... 
arXiv:1911.08777v2 fatcat:vqrij6f4uffmrgn5ow6lvxxmhq

Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation [article]

Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia
2019 arXiv   pre-print
The two branches interact with each other and cooperate in segmentation. Decent experimental results on several 3D semantic labeling datasets demonstrate the effectiveness of our work.  ...  For each edge in the final graph, we predict a label to indicate the semantic consistency of the two connected points to enhance point prediction.  ...  Another approach is to use multi-view 2D images, to which 2D convolutions [18, 11] can be directly applied.  ... 
arXiv:1909.10469v1 fatcat:iqeq3n7mwfcdrfeqwlexcydgfa

Neuroplastic graph attention networks for nuclei segmentation in histopathology images [article]

Yoav Alon, Huiyu Zhou
2022 arXiv   pre-print
In experimental evaluation, our framework outperforms ensembles of state-of-the-art neural networks, with a fraction of the neurons typically required, and sets new standards for the segmentation of new  ...  We propose a novel architecture for semantic segmentation of cell nuclei robust to differences in experimental configuration such as staining and variation of cell types.  ...  features for accurate segmentation in multiple levels of magnification. • Development of a Residual Graph Attention Network architecture based on an advanced self-attention mechanism weighting attention  ... 
arXiv:2201.03669v1 fatcat:uujxbsnmlfbfbagnm5umjjejka

High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting

Yifan Liu, Qigang Zhu, Feng Cao, Junke Chen, Gang Lu
2021 ISPRS International Journal of Geo-Information  
Semantic segmentation has been widely used in the basic task of extracting information from images.  ...  In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems.  ...  In particular, on behalf of all the authors, I would like to thank Zhang Shuaishuai again for vividly providing me with ideas for revising the paper and replying to the reviewers' opinions during the review  ... 
doi:10.3390/ijgi10040241 fatcat:wqkht2oavzesngpnlpgrhjh75y

Real-time RGB-D semantic keyframe SLAM based on image segmentation learning from industrial CAD models

Howard Mahe, Denis Marraud, Andrew I. Comport
2019 2019 19th International Conference on Advanced Robotics (ICAR)  
The semantic segmentation network was fine tuned for the given use case and was trained in a semisupervised manner using noisy labels.  ...  The approach is shown to robustly construct a 3D map with associated class labels relevant to robotic tasks.  ...  The approach is shown to robustly construct a 3D map with associated class labels relevant to robotic tasks.  ... 
doi:10.1109/icar46387.2019.8981549 dblp:conf/icar/MaheMC19 fatcat:qaflp2owlfaffdx2wk4kcp7d5i

Deep Learning for 3D Point Cloud Understanding: A Survey [article]

Haoming Lu, Humphrey Shi
2021 arXiv   pre-print
, segmentation, detection, tracking, flow estimation, registration, augmentation and completion), together with commonly used datasets, metrics and state-of-the-art performances.  ...  While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points.  ...  The approach consists of an encoder and an decoder, where the encoder is constructed from multi-scale graphs, and the decoder is constructed for three unsupervised tasks (clustering, self-supervised classification  ... 
arXiv:2009.08920v2 fatcat:qiuhs6v345bpffzjk2nvhgbfvq

MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding [article]

Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen
2022 arXiv   pre-print
In this paper, we introduce MASS - a Multi-Attentional Semantic Segmentation model specifically built for dense top-view understanding of the driving scenes.  ...  While most previous works focus on sparse segmentation of the LiDAR input, dense output masks provide self-driving cars with almost complete environment information.  ...  view or a bird's eye view, and thereby inherit the advancements in image semantic segmentation using 2D fully convolutional networks.  ... 
arXiv:2107.00346v2 fatcat:36a6n5znivch5n5g4ssffh7zqa
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