Hierarchically Gated Deep Networks for Semantic Segmentation

2016 ◽  
Author(s):  
Guo-Jun Qi
Keyword(s):  
Semantic Segmentation ◽  
Deep Networks

scholarly journals Scene-Aware Deep Networks for Semantic Segmentation of Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69184-69193 ◽  
Author(s):  
Zhike Yi ◽  
Tao Chang ◽  
Shuai Li ◽  
Ruijun Liu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Deep Networks ◽  
Segmentation Of Images

scholarly journals A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks

2019 ◽  
Vol 11 (6) ◽  
pp. 684 ◽  
Author(s):  
Maria Papadomanolaki ◽  
Maria Vakalopoulou ◽  
Konstantinos Karantzalos
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Novel Object ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Object Based ◽  
Deep Networks ◽  
Very High

Deep learning architectures have received much attention in recent years demonstrating state-of-the-art performance in several segmentation, classification and other computer vision tasks. Most of these deep networks are based on either convolutional or fully convolutional architectures. In this paper, we propose a novel object-based deep-learning framework for semantic segmentation in very high-resolution satellite data. In particular, we exploit object-based priors integrated into a fully convolutional neural network by incorporating an anisotropic diffusion data preprocessing step and an additional loss term during the training process. Under this constrained framework, the goal is to enforce pixels that belong to the same object to be classified at the same semantic category. We compared thoroughly the novel object-based framework with the currently dominating convolutional and fully convolutional deep networks. In particular, numerous experiments were conducted on the publicly available ISPRS WGII/4 benchmark datasets, namely Vaihingen and Potsdam, for validation and inter-comparison based on a variety of metrics. Quantitatively, experimental results indicate that, overall, the proposed object-based framework slightly outperformed the current state-of-the-art fully convolutional networks by more than 1% in terms of overall accuracy, while intersection over union results are improved for all semantic categories. Qualitatively, man-made classes with more strict geometry such as buildings were the ones that benefit most from our method, especially along object boundaries, highlighting the great potential of the developed approach.

scholarly journals Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks

2020 ◽  
Author(s):  
Samik Banerjee ◽  
Lucas Magee ◽  
Dingkang Wang ◽  
Xu Li ◽  
Bingxing Huo ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Performance ◽  
Large Scale ◽  
Image Data ◽  
Semantic Segmentation ◽  
Scientific Data ◽  
Error Rates ◽  
Topological Data Analysis ◽  
Hybrid Architecture ◽  
Deep Networks

Understanding of neuronal circuitry at cellular resolution within the brain has relied on tract tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.

scholarly journals DropConnect is effective in modeling uncertainty of Bayesian deep networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aryan Mobiny ◽  
Pengyu Yuan ◽  
Supratik K. Moulik ◽  
Naveen Garg ◽  
Carol C. Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Prediction Accuracy ◽  
State Of The Art ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Learning Technology ◽  
Network Architectures ◽  
Uncertainty Estimates ◽  
Deep Networks

AbstractDeep neural networks (DNNs) have achieved state-of-the-art performance in many important domains, including medical diagnosis, security, and autonomous driving. In domains where safety is highly critical, an erroneous decision can result in serious consequences. While a perfect prediction accuracy is not always achievable, recent work on Bayesian deep networks shows that it is possible to know when DNNs are more likely to make mistakes. Knowing what DNNs do not know is desirable to increase the safety of deep learning technology in sensitive applications; Bayesian neural networks attempt to address this challenge. Traditional approaches are computationally intractable and do not scale well to large, complex neural network architectures. In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights. This method called Monte Carlo DropConnect (MC-DropConnect) gives us a tool to represent the model uncertainty with little change in the overall model structure or computational cost. We extensively validate the proposed algorithm on multiple network architectures and datasets for classification and semantic segmentation tasks. We also propose new metrics to quantify uncertainty estimates. This enables an objective comparison between MC-DropConnect and prior approaches. Our empirical results demonstrate that the proposed framework yields significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.

scholarly journals Circle-U-Net: An Efficient Architecture for Semantic Segmentation

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 159
Author(s):  
Feng Sun ◽  
Ajith Kumar V ◽  
Guanci Yang ◽  
Ansi Zhang ◽  
Yiyun Zhang
Keyword(s):  
State Of The Art ◽  
Semantic Segmentation ◽  
Proposed Model ◽  
Segmentation Methods ◽  
Deep Networks ◽  
Improved Accuracy ◽  
Better Than

State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed model includes circle connect layers, which is the backbone of ResUNet-a architecture. The model possesses a contracting part with residual bottleneck and circle connect layers that capture context and expanding paths, with sampling layers and merging layers for a pixel-wise localization. The results of the experiment show that the proposed Circle-U-Net achieves an improved accuracy of 5.6676%, 2.1587% IoU (Intersection of union, IoU) and can detect 67% classes greater than U-Net, which is better than current results.

scholarly journals Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder–decoder deep networks

2020 ◽  
Vol 2 (10) ◽  
pp. 585-594
Author(s):  
Samik Banerjee ◽  
Lucas Magee ◽  
Dingkang Wang ◽  
Xu Li ◽  
Bing-Xing Huo ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Deep Networks ◽  
Neuroanatomical Data
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