A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Class-wise Dynamic Graph Convolution for Semantic Segmentation
[article]
2020
arXiv
pre-print
Preserved Fulltext
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 ...
Based on the proposed CDGC module, we further introduce the Class-wise Dynamic Graph Convolution Network(CDGCNet), which consists of two main parts including the CDGC module and a basic segmentation network ...
Fig. 2 . 2 An Overview of the Class-wise Dynamic Graph Convolution Network.
Fig. 3 . 3 The details of Class-wise Dynamic Graph Convolution Module. ...
arXiv:2007.09690v1
fatcat:2nkmvl73dzcarnlp6kogcwvevu
Dynamic-structured Semantic Propagation Network
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
During training, DSSPN performs the dynamic-structured neuron computation graph by only activating a sub-graph of neurons for each image in a principled way. ...
In this paper, we propose a Dynamic-Structured Semantic Propagation Network (DSSPN) that builds a semantic neuron graph by explicitly incorporating the semantic concept hierarchy into network construction ...
The basic convolutional features are propagated into a dynamic-structured semantic neuron graph for hierarchical pixel-wise recognition. ...
arXiv:1803.06067v1
fatcat:n5ory4illreqvklfq3t67n5tbm
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Through the fine-grained clusters of the foreground objects from the semantic segmentation backbone, over-segmentation priors are generated and subsequently processed by 3D sparse convolution to embed ...
Our new design consists of a novel instance-level network to process the semantic results by constructing a graph convolutional network to identify objects (foreground), which later on are fused with the ...
(AF) 2 -S3Net provides semantic segmentation results using efficient feature attention and fusion at different scales, making the model able to predict semantics for all classes, especially smaller dynamic ...
arXiv:2108.08401v1
fatcat:akhqf2euxjetzjccbtwt6t2csm
Hierarchical Pyramid Representations for Semantic Segmentation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Understanding the context of complex and cluttered scenes is a challenging problem for semantic segmentation. ...
Our key idea is the recursive segmentation in different hierarchical regions based on a predefined number of regions and the aggregation of the context in these regions. ...
Acknowledgement This work is partially supported by Grants-in-aid for Promotion of Regional Industry-University-Government Collaboration from Cabinet Office, Japan. ...
arXiv:2104.01792v1
fatcat:a4a7pwaydrgmnag2h4ftvkjbhm
Adaptive Graph Convolution for Point Cloud Analysis
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. ...
Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic ...
The class IoU (mcIoU) is the mean IoU over all shape categories. We also show the class-wise segmentation results. Our model achieves the state-of-the-art performance compared with other methods. ...
arXiv:2108.08035v2
fatcat:65igt5oemzfwtizm532hgnwfnu
DeepGCNs: Can GCNs Go as Deep as CNNs?
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, borrow concepts from CNNs, and apply them in training. ...
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation ...
The authors thank Adel Bibi and Guocheng Qian for their help with the project. ...
arXiv:1904.03751v2
fatcat:kkcxgwcchvb3nc7mxfdvrhaoyy
SEMANTIC SEGMENTATION FOR SELF DRIVING CARS
2021
International Journal of Engineering Applied Sciences and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
So, therefore here we are presenting deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. ...
semantic segmentation. ...
The goal of semantic segmentation is to label the similar images as a particular semantic class. It performs pixel-wise classification of images. ...
doi:10.33564/ijeast.2021.v06i03.027
fatcat:dnikpljldvb4bdyova2nzo7eyu
Point Attention Network for Semantic Segmentation of 3D Point Clouds
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We propose a point attention network that learns rich local shape features and their contextual correlations for 3D point cloud semantic segmentation. ...
predicting dense labels for 3D point cloud segmentation. ...
, which is important for semantic segmentation. ...
arXiv:1909.12663v1
fatcat:vpk2ae4m7zfqppy2c43o2fxd5i
Unifying Instance and Panoptic Segmentation with Dynamic Rank-1 Convolutions
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
for both semantic and instance segmentation. ...
In this paper, we demonstrate that adding a single classification layer for semantic segmentation, fully-convolutional instance segmentation networks can achieve state-of-the-art panoptic segmentation ...
Even though it is sufficient for class agnostic instance segmentation, this prohibits sharing the bottom output for semantic segmentation. ...
arXiv:2011.09796v1
fatcat:7pxikyerkfdkdhasa72t3rwaf4
CpT: Convolutional Point Transformer for 3D Point Cloud Processing
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We evaluate our model on standard benchmark datasets such as ModelNet40, ShapeNet Part Segmentation, and the S3DIS 3D indoor scene semantic segmentation dataset to show that our model can serve as an effective ...
Our novel CpT block builds over local neighbourhoods of points obtained via a dynamic graph computation at each layer of the networks' structure. ...
The proposed CpT architecture for the classification and segmentation tasks. Upper path: classification over c classes. Lower path: segmentation over the p semantic labels. ...
arXiv:2111.10866v1
fatcat:qp7ccsxfpzhd3fxbkxn4t5dhbm
DeepGCNs: Making GCNs Go as Deep as CNNs
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Graph Convolutional Networks (GCNs) offer an alternative that allows for non-Eucledian data input to a neural network. ...
Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, ...
ACKNOWLEDGMENTS The authors thank Adel Bibi for his help with the project. ...
arXiv:1910.06849v2
fatcat:4rjqgbw3y5ae7hhthuxnwjjodi
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To this end, we propose a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS. ...
The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic training scenario. ...
Semantic Segmentation Semantic segmentation is a classical computer vision task which aims to give pixel-wise prediction for an input image. ...
arXiv:2204.10638v1
fatcat:fulauv6c25gqjcrw3lauo5ufmq
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
but relying on accurate detection of skyline Saurer16 and other two proposed for general semantic segmentation -- Fully Convolutional Networks (FCN) Long15 and SegNetBadrinarayanan15. ...
Detecting such a segmenting boundary fully autonomously would definitely be a step forward for these localization approaches. ...
maintaining sharp boundary delineation which is essential for pixel-wise segmentation of small/rare classes. ...
arXiv:1805.08105v1
fatcat:rfp4see2sfcmdpin253glo4r4m
Using Image Priors to Improve Scene Understanding
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
The prior fusion model improves the accuracy over the non-prior baseline from 69.1% to 73.3% for dynamic classes, and from 88.2% to 89.1% for static classes. ...
Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. ...
Conclusions We have demonstrated that the addition of prior knowledge to a deep convolutional network can increase the performance of semantic segmentation, particularly for dynamic objects. ...
arXiv:1910.01198v1
fatcat:xxa2qptdhraidfdr5n6e7meepe
Deep Parametric Continuous Convolutional Neural Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2101.06742v1 fatcat:fr7jtl5jojc7jmyxbrjlvfpeau
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. ...
Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes, and lidar motion estimation of driving scenes. ...
Please refer to Fig. 2 for an example of the computation graph of a single layer, and to Fig. 3 for an example of the network architecture employed for our indoor semantic segmentation task. ...
doi:10.1109/cvpr.2018.00274
dblp:conf/cvpr/WangSMPU18
fatcat:augqqmf56vcqtcof3bi2x64m6e
« Previous
Showing results 1 — 15 out of 4,156 results