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Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation [article]

Haitian Zheng, Yebin Liu, Mengqi Ji, Feng Wu, Lu Fang
2015 arXiv   pre-print
This paper proposes a convolutional neural network that can fuse high-level prior for semantic image segmentation.  ...  segmentation from the high level prior and input image.  ...  To take advantage of the high-level semantic information, we propose in this paper a deep neutral networks that can integrate high-level prior for high quality semantic image segmentation.  ... 
arXiv:1511.06988v1 fatcat:l4o3kkagvnhkhkgdoszkuv2gfq

Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment [article]

Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy, Odair Fernandes
2020 arXiv   pre-print
The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation.  ...  In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation.  ...  Three major issues with the application of deep convolutional neural networks for semantic segmentation are: the lose of information or decrement of signal resolution, objects with different sizes, and  ... 
arXiv:2009.01193v2 fatcat:yzruve2vhjabpltly4qg3ozdim

Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment

Tashnim Chowdhury, Maryam Rahnemoonfar, Robin Murphy, Odair Fernandes
2020 2020 IEEE International Conference on Big Data (Big Data)  
The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation.  ...  In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation.  ...  Three major issues with the application of deep convolutional neural networks for semantic segmentation are: the lose of information or decrement of signal resolution, objects with different sizes, and  ... 
doi:10.1109/bigdata50022.2020.9377916 fatcat:6dq5kmzz6bhgdmu4gbhriz5bju

DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

Xi Li, Liming Zhao, Lina Wei, Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang
2016 IEEE Transactions on Image Processing  
In this paper, we propose a multi-task deep saliency model based on a fully convolutional neural network (FCNN) with global input (whole raw images) and global output (whole saliency maps).  ...  correlations between saliency detection and semantic image segmentation.  ...  Architecture of the proposed fully convolutional neural network for training.  ... 
doi:10.1109/tip.2016.2579306 pmid:27305676 fatcat:6rnohyiqofc2jfeauaq3b2cllq

A Brief Survey on Weakly Supervised Semantic Segmentation

Youssef Ouassit, Soufiane Ardchir, Mohammed Yassine El Ghoumari, Mohamed Azouazi
2022 International Journal of Online and Biomedical Engineering (iJOE)  
This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research  ...  However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy.  ...  Fully Convolution Neural Network was the first segmentation models that based on Convolution Neural Network and have high performance and remarkable accuracy in semantic segmentation task [1] .  ... 
doi:10.3991/ijoe.v18i10.31531 fatcat:6klflaiecrdgrizzlpgybimt6q

Learning deep representations for semantic image parsing: a comprehensive overview

Lili Huang, Jiefeng Peng, Ruimao Zhang, Guanbin Li, Liang Lin
2018 Frontiers of Computer Science  
In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation  ...  The recent application of deep representation learning has driven this field into a new stage of development.  ...  learning, such as convolutional neural networks (CNNs), recurrent neural networks, and recursive neural networks (RNNs); and 3) the integration of the two methods to complement each other.  ... 
doi:10.1007/s11704-018-7195-8 fatcat:p5hvfwhl5rbork5vf4rpnx3h6u

Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [article]

Zhongyi Han, Benzheng Wei, Yilong Yin, Shuo Li
2020 arXiv   pre-print
segmentation of spinal structures with high complexity and variability.  ...  Concretely, we design an adversarial graph network that interpolates a symbolic graph reasoning module into a generative adversarial network through embedding prior domain knowledge, achieving semantic  ...  We compare the semantic segmentation ability of our neural symbolic learning framework (NSL) with several state-of-the-art semantic segmentation networks as follows. • Fully Convolutional Network (FCN)  ... 
arXiv:2004.13577v1 fatcat:oh5aka5zr5be3ipd7qqnyhikzy

Survey on Semantic Segmentation using Deep Learning Techniques

Fahad Lateef, Yassine Ruichek
2019 Neurocomputing  
Semantic segmentation is a challenging task in computer vision systems.  ...  Many of these methods have been built using the deep learning paradigm that has shown a salient performance.  ...  ACKNOWLEDGMENT The authors express their gratitude to University Technology Belfort-Montbeliard and Higher Education Commission of Pakistan for providing the support and necessary requirement for completion  ... 
doi:10.1016/j.neucom.2019.02.003 fatcat:aelsfl7unvdw5j2rtyqhtgqrsm

Fast-SCNN: Fast Semantic Segmentation Network [article]

Rudra P K Poudel, Stephan Liwicki, Roberto Cipolla
2019 arXiv   pre-print
In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation  ...  Building on existing two-branch methods for fast segmentation, we introduce our 'learning to downsample' module which computes low-level features for multiple resolution branches simultaneously.  ...  In this work we propose fast segmentation convolutional neural network Fast-SCNN, an above real-time semantic segmentation algorithm merging the two-branch setup of prior art [21, 34, 17, 36] , with the  ... 
arXiv:1902.04502v1 fatcat:i6n7ljo7zjc3tlqjrj7i7r7bgq

Generic Pixel Level Object Tracker Using Bi-Channel Fully Convolutional Network [chapter]

Zijing Chen, Jun Li, Zhe Chen, Xinge You
2017 Lecture Notes in Computer Science  
In this work, we propose a novel bi-channel fully convolutional neural network to tackle the generic pixel-level object tracking problem.  ...  By capturing and fusing both low-level and high-level temporal information, our network is able to produce pixel-level foreground mask of the target accurately.  ...  High-level Semantic Branch In high-level branch, we introduce the fully convolutional neural network to update the parsing of object / scene semantics in each new frame regardless of its category.  ... 
doi:10.1007/978-3-319-70087-8_69 fatcat:osadgd7cunethk5mpsapqtaltq

Convolutional Neural Network Based Medical Image Classifier

2019 International journal of recent technology and engineering  
This includes prior knowledge of testing samples and training samples, using Convolutional Neural Networks (CNN).  ...  In order to classify the Brain tumour images the semantic level classification and segmentation network techniques are applied.  ...  In order to classify the Brain tumour images the semantic level classification and segmentation network techniques are applied.  ... 
doi:10.35940/ijrte.c6810.098319 fatcat:6sdgqgimxvfttj4yagfuom4nty

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation [article]

Bowen Zhang, Yifan Liu, Zhi Tian, Chunhua Shen
2021 arXiv   pre-print
Semantic segmentation requires per-pixel prediction for a given image.  ...  The desired semantic labels can be efficiently decoded from the neural representations, resulting in high-resolution semantic segmentation predictions.  ...  This avoids the separable learning process as done in [THSY19] . Thus, our method is termed dynamic neural representation decoder (NRD) for semantic segmentation.  ... 
arXiv:2107.14428v1 fatcat:fdeq5aitpjcibd4ej6thqziu5q

P-LINKNET: LINKNET WITH SPATIAL PYRAMID POOLING FOR HIGH-RESOLUTION SATELLITE IMAGERY

Y. Ding, M. Wu, Y. Xu, S. Duan
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In order to solve this problem, we proposed a deep learning model with a spatial pyramid pooling module based on the LinkNet.  ...  Although extensively studied in the past years, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing  ...  In the FCN, feature maps with high-level semantics but low resolutions are genreated by down-sampling features using multiple pooling or convolutions with strides .  ... 
doi:10.5194/isprs-archives-xliii-b3-2020-35-2020 fatcat:vxae26nusvg5pjdiugnqlhe62u

Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning

Ariyo Oluwasammi, Muhammad Umar Aftab, Zhiguang Qin, Son Tung Ngo, Thang Van Doan, Son Ba Nguyen, Son Hoang Nguyen, Giang Hoang Nguyen, Dan Selisteanu
2021 Complexity  
In this survey, we deliberate on the use of deep learning techniques on the segmentation analysis of both 2D and 3D images using a fully convolutional network and other high-level hierarchical feature  ...  With the emergence of deep learning, computer vision has witnessed extensive advancement and has seen immense applications in multiple domains.  ...  ExFuse aims to connect the gap between low-and highlevel features in convolutional networks by introducing semantic information at the lower-level features as well as high resolutions into the high-level  ... 
doi:10.1155/2021/5538927 fatcat:4yae4kjqdne6vaqus5plna4mwm

Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges [article]

Mennatullah Siam, Sara Elkerdawy, Martin Jagersand, Senthil Yogamani
2017 arXiv   pre-print
Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving.  ...  Second, the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed.  ...  Fully Convolutional Networks(FCN) The area of semantic segmentation using convolutional neural networks witnessed tremendous progress recently.  ... 
arXiv:1707.02432v2 fatcat:mksnyowbojgm3i3vcp7nwpdkcq
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