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SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation
[article]
2021
arXiv
pre-print
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In this work, we focus on designing a point local aggregation function that yields parameter efficient networks for 3D point cloud semantic segmentation. ...
We explore the idea of using learnable neighbor-to-grid soft assignment in grid-based aggregation functions. ...
In an analogous way, 2D semantic segmentation groups pixels into semantically meaningful classes. ...
arXiv:2012.14929v2
fatcat:xiilgzdnazbs5lgcuagnke7fxm
Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions
[article]
2018
arXiv
pre-print
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Semantic image segmentation is one of the most challenged tasks in computer vision. ...
We evaluate our model on three major segmentation datasets: CamVid, PASCAL VOC and ADE20K. ...
In FCCN, we have proposed a soft cost function for training a segmentation network [16] . Soft cost function adds a weight on each semantic area, while always keeps the weight background to be one. ...
arXiv:1801.01317v1
fatcat:tc4acti5qfdjre7kfiil2a2y7u
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
[article]
2022
arXiv
pre-print
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In this paper, we propose a new object-blend method(short in soft-CP) that combines the Copy-Paste augmentation method for semantic segmentation of medical lesions offline, ensuring the correct edge information ...
Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-knowledge and time-consuming. ...
Our method, soft copy-paste (short in Soft-CP), allows users to augment credible medical lesions for semantic segmentation tasks effectively. lesions. ...
arXiv:2203.10507v1
fatcat:qdw46tkjefhohpfkqjqpwxjo5a
Unsupervised Semantic Segmentation Method of User Interface Component of Games
2022
Intelligent Automation and Soft Computing
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Our methodology can help researchers who need to extract semantic information from game image data. It can also be used for UI prototyping in the game industry. ...
Therefore, this paper proposes a game UI segmentation technique based on unsupervised learning. ...
Previous models, especially fully convolutional network (FCN), used a single score map for semantic segmentation. ...
doi:10.32604/iasc.2022.019979
fatcat:v7v53o4vknfqrkcdj6f57kbrxm
A novel vSLAM framework with unsupervised semantic segmentation based on adversarial transfer learning
2020
Applied Soft Computing
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Semantic segmentation results contain some wrong 599 semantic categories and only the hand part of the human body is successfully segmented.
600 The common semantic segmentation models without special ...
to obtain the semantic segmentation outputs Ls and Hs. ...
doi:10.1016/j.asoc.2020.106153
fatcat:gezfd66gananrbsfmk45cj2ksy
Air, bone and soft-tissue Segmentation on 3D brain MRI Using Semantic Classification Random Forest with Auto-Context Model
[article]
2019
arXiv
pre-print
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The proposed semantic classification random forest (SCRF) method consists of a training stage and a segmentation stage. ...
The proposed segmentation technique could be a useful tool to segment bone, air and soft tissue, and have the potential to be applied to attenuation correction of PET/MRI system, MRI-only radiation treatment ...
(b1) is the MRI image, and (a1, c1, d1) show the binary segmentation for each material (air, bone and soft tissue) based on CT segmentation. ...
arXiv:1911.09264v2
fatcat:a2kekiuwkfbuhnxrbggjqtibzy
Automated Crack Detection via Semantic Segmentation Approaches Using Advanced U-Net Architecture
2022
Intelligent Automation and Soft Computing
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In this study, we conducted image segmentation using various crack datasets by applying the advanced architecture of U-Net. ...
Overall, the results of the semantic segmentation tasks using ResU-Net and EfficientU-Net were similar. ...
[2] proposed a vision-oriented crack detection system with a deep semantic segmentation network. Additionally, Lee et al. ...
doi:10.32604/iasc.2022.024405
fatcat:qiyszir4tjan3cmmblelrntjdq
Task Based Semantic Segmentation of Soft X-ray CT Images Using 3D Convolutional Neural Networks
2020
Microscopy and Microanalysis
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Semantic segmentation refers to the process of linking each pixel in an image to a class label, for example, cell phenotype, membrane, nucleus, or mitochondria. ...
In Soft X-ray Tomography (SXT), segmentation is based on the measured Linear Absorption Coefficient (LAC) and guided and confirmed by structural cues, sub-cellular location, together with data from other ...
In Soft X-ray Tomography (SXT), segmentation is based on the measured Linear Absorption Coefficient (LAC) and guided and confirmed by structural cues, sub-cellular location, together with data from other ...
doi:10.1017/s1431927620023983
fatcat:buzzjwkfvjg4lhwrspitehs6im
DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
[article]
2021
pre-print
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Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain. ...
To address this issue, we propose a novel Dual Soft-Paste (DSP) method in this paper. ...
• We propose a novel Dual Soft-Paste (DSP) method to create intermediate domains and facilitate domain alignment for semantic segmentation. ...
doi:10.1145/3474085.3475186
arXiv:2107.09600v1
fatcat:ka2mm4tayrdmlozq2xd2pcvuja
Semantic Segmentation of Clothes in the Context of Soft Biometrics Using Deep Learning Methods
2020
Anais do 14. Congresso Brasileiro de Inteligência Computacional
unpublished
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Semantic segmentation of clothes is still a challenge for researchers because of the wide variety of clothing styles, layering, and shapes. ...
This work presents an approach for clothing semantic segmentation tasks using the Feature Pyramid Network (FPN) with the EfficientNet as the backbone. ...
In this paper, we propose an approach based on the Feature Pyramid Network (FPN) for clothing semantic segmentation in the context of soft biometrics. ...
doi:10.21528/cbic2019-77
fatcat:roty2ahuxjhopi6mvd5i5dolle
The Research on Chinese Word Segmentation System with Semantic Annotations Information
2014
Proceedings of the 2nd International Conference on Soft Computing in Information Communication Technology
unpublished
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; word segmentation; semantic annotations I. ...
Aim to the existing Chinese word segmentation system only focus on grammar annotation, this paper will learning the rules of semantic annotation based on ID3 and feature selection based on CHI , the preliminary ...
Calculating the probability of the word appears through a variety of parameters in the process of word segmentation, the maximum probability as International Conference on on Soft Computing in Information ...
doi:10.2991/scict-14.2014.46
fatcat:5zg3igadtnclhdu76o7nxdz274
Structure Segmentation of Dental Tissue Based on Semantic Characteristics
2017
DEStech Transactions on Computer Science and Engineering
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Aiming at this problem, a novel dental tissue structural segmentation method based on semantic characteristics was proposed, which not only focus on dental tissue semantic segmentation, but also the instance ...
At last, the markercontrolled watershed transform was employed to complete semantic segmentation. The experimental results show that the proposed method is robust and has higher accuracy. ...
Items
P
R
F
Hard Tissue Semantic Segmentation
91.3%
99.0%
94.9%
Soft Tissue Semantic Segmentation
90.3%
89.0%
89.3%
Tooth Instance level Segmentation
90.8%
98.5%
94.4% ...
doi:10.12783/dtcse/aiea2017/15029
fatcat:mqzmvqo6erfgtkgwqghg2irnzy
Deep Aggregation Net for Land Cover Classification
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In particular, we introduce soft semantic labels and graphbased fine tuning in our proposed network for improving the segmentation performance. ...
Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. ...
We utilize soft semantic labels and graph-based fine tuning in our proposed network. ...
doi:10.1109/cvprw.2018.00046
dblp:conf/cvpr/KuoTYLW18
fatcat:kitsgx5dljgzbpvh6ofhlkyw5q
Visual Vocabulary with a Semantic Twist
[chapter]
2015
Lecture Notes in Computer Science
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Furthermore, we also introduce a fast, and near state of the art, semantic segmentation algorithm. ...
We describe a method, SemanticSIFT, which takes account of local image semantic content (such as grass and sky) in matching, and thereby eliminates many false matches. ...
For this reason we introduce our own semantic segmentation method, dubbed Fast Semantic Segmentation via Soft Segments (FSSS), which is described next. ...
doi:10.1007/978-3-319-16865-4_12
fatcat:bgdjpuvkd5bknhgmlvg2zcvyaq
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
[article]
2017
arXiv
pre-print
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In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to ...
Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. ...
Using soft masks helps mitigate this, but there is certainly scope to improve semantic segmentation, or to develop methods dedicated to generating soft semantic masks. ...
arXiv:1708.09641v1
fatcat:urf6hxpvqfcmdfylhtwevze5eu
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