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SALA: Soft Assignment Local Aggregation for Parameter Efficient 3D Semantic Segmentation [article]

Hani Itani, Silvio Giancola, Ali Thabet, Bernard Ghanem
2021 arXiv   pre-print
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]

Tao Yang, Yan Wu, Junqiao Zhao, Linting Guan
2018 arXiv   pre-print
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]

Pingping Dai, Licong Dong, Ruihan Zhang, Haiming Zhu, Jie Wu, Kehong Yuan
2022 arXiv   pre-print
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

Shinjin Kang, Jongin Choi
2022 Intelligent Automation and Soft Computing  
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

Sheng Jin, Liang Chen, Rongchuan Sun, Seán McLoone
2020 Applied Soft Computing  
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]

Xue Dong, Yang Lei, Sibo Tian, Yingzi Liu, Tonghe Wang, Tian Liu, Walter J. Curran, Hui Mao, Hui-Kuo Shu, Xiaofeng Yang
2019 arXiv   pre-print
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

Honggeun Ji, Jina Kim, Syjung Hwang, Eunil Park
2022 Intelligent Automation and Soft Computing  
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

Axel Ekman, Jian-Hua Chen, Gerry Mc Dermott, Mark A. Le Gros, Carolyn Larabell
2020 Microscopy and Microanalysis  
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]

Li Gao, Jing Zhang, Lefei Zhang, Dacheng Tao
2021 pre-print
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

Andrei de Souza Inácio, Anderson Brilhador, Heitor Silvério Lopes
2020 Anais do 14. Congresso Brasileiro de Inteligência Computacional   unpublished
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

Xian-Yi Cheng, Wei Kang, Jie Zhang, Quan Shi
2014 Proceedings of the 2nd International Conference on Soft Computing in Information Communication Technology   unpublished
; 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  
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

Tzu-Sheng Kuo, Keng-Sen Tseng, Jia-Wei Yan, Yen-Cheng Liu, Yu-Chiang Frank Wang
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
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]

Relja Arandjelović, Andrew Zisserman
2015 Lecture Notes in Computer Science  
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]

Huihuang Zhao, Paul L. Rosin, Yu-Kun Lai
2017 arXiv   pre-print
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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