Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting . The proposed Fully Convolutional Network ( FCN ) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive . The global average pooling in our convolutional model enables the exploitation of the Class Activation Map ( CAM ) to find out the contributing region in the raw data for the specific labels . Furthermore , based on the prediction results , the post-processing methods of 3D morphological closed operation and volume analysis are employed to smooth the surface of vessels and eliminate noise blocks , respectively . Compared with existing 3D DenseNet , FCN and 3D U-Net , the average Dice coefficients of our method in hepatic veins and portal veins segmentation are 71.7 % and 76.5 % respectively , which are superior to 55.3 % and 53.9 % of the 3D DenseNet , 60.2 % and 75.6 % of the FCN , and 66.4 % and 73.9 % of the 3D U-Net . Meanwhile , the cross validation results prove that our method is accurate and stable for liver vessel extraction . However , various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks , and hence , impose restrictions on feasible crossbar sizes . In this paper , we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance , neuron resistance , and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network ( FCN ) and convolutional neural network ( CNN ) trained with Backpropagation algorithm . We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84 % for FCNs and 62.4 % for CNNs when implemented on nonideal crossbars for relevant nonideality ranges . In this paper , we propose a simple but effective framework to automatically extract accurate boundaries of synaptic cleft regions . Our approach concatenates a novel contour growing algorithm to a fully convolutional network ( FCN ) , so that it takes both advantages of large receptive field of FCNs and fine-level localization of contour evolution . The contour growing algorithm is based on the flexible evolving tension and synchronous growing controlling to localize the opening contour of clef region . Therefore , how to dig out valuable information in these images and help doctors to make diagnosis more accurately and quickly have always been the concern of researchers . In this paper , we have made some improvements to the FCN network and incorporated Inception Architecture into it to build several convolutional neural networks . In our experiments , we trained the networks in IBSR dataset and contrasted the results with some classical methods . Secondly , the mathematical morphology reconstruction operation is used to improve the segmentation result to get the accurate localization of the insulator areas . Finally , the FCN network is used again to detect the insulator fault and obtain the fault region . Experiments show that the proposed SOFCN is not only able to obtain accurate insulator region , but also able to effectively suppress the interference of noninsulator region . Secondly , focal loss was applied to make the training focus on wrongly-classified pixels and hence achieve good performance on small object segmentation . Comparisons between FCN , Weighted FCN , Fully-weighted FCN and Focal FCN were tested on customized stent graft marker segmentation . Motivated by this observation that accurately salient object detection is related to both foreground and background , we proposed a novel cooperative attention mechanism that jointly considers reciprocal relationships between background and foreground for efficient salient object detection . Concretely , we first aggregate the features at each side-out of traditional dilated FCN to extract the initial foreground and background local responses respectively . Then taking these responses as input , reciprocal attention module adaptively models the nonlocal dependencies between any two pixels of the foreground and background features , which is then aggregated with local features in a mutual reinforced way so as to enhance each branch to generate more discriminative foreground and background saliency map . In FPGA , an encoder – decoder-based FCN is utilized to consider the global spatial information by processing the whole image , which results in fast inference . However , it is difficult to directly utilize the encoder – decoder-based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples . To solve the divergence problem and maintain the FCNs abilities of fast inference and global spatial information mining , a global stochastic stratified ( GS2 ) sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples . Large working data sets result in heavy accesses across the memory hierarchy , moreover large amounts of intermediate data are also generated due to the large number of multiply-and-accumulate ( MAC ) operations for both CNN and FCN . Even when binary-based DNN [ 3 ] are used , the required CNN and FCN operations result in a major memory I/O bottleneck for AI edge devices . Each level of the FCN cascade is a multi-scale fully-convolutional network , which generates scores at different locations and in different scales . A score map is generated after each FCN stage . Probable regions of face are selected and fed to the next stage . Processing in the sinogram domain directly can make the effective information maximum extent preserved . In this paper , we propose a novel method based on full convolutional network ( FCN ) for metal artifact reduction in the sinogram domain . The networks we introduced use the complete sinogram data to learn a mapping function to correct the metal-corrupted sinogram data . Next , we demonstrate improved accuracy on PASCAL VOC 2011 - 2 , NYUDv2 , SIFT Flow , and PASCAL - Context . Finally , we analyze design choices , examine what cues can be learned by an FCN , and calculate recognition bounds for semantic segmentation . section : Related Work A number of recent approaches improve detection and segmentation by using different layers in a ConvNet . FCN [ reference ] sums partial scores for each category over multiple scales to compute semantic segmentations . Hypercolumns [ reference ] uses a similar method for object instance segmentation . subsubsection : High Quality Semantic Segmentation FCN is the pioneer work to replace the last fully - connected layers in classification with convolution layers . DeepLab and used dilated convolution to enlarge the receptive field for dense labeling . We encode both visual and contextual information to represent a superpixel . First , we use the FCN - 8s pre - trained on the PASCAL CONTEXT dataset , which has 59 distinct classes , to obtain 59 detection scores for each pixel . We then average them within each superpixel . In the latter case , it improves per - pixel accuracy from 54 % to 82 % , nearly closing the gap to the target - trained model . Our experiments confirm that domain adaptation can benefit greatly from cycle - consistent pixel transformations , and that this is especially important for pixel - level semantic segmentation with contemporary FCN architectures . Further , we show that adaptation at both the pixel and representation level can offer complementary improvements with joint pixel - space and feature adaptation leading to the highest performing model for digit classification tasks . However , they may also be restricted to rectangular bounding box constraints even with appropriate rotation . Different from these methods , FCN based approaches generate text / non - text map which classifies text at the pixel level . Though it may be suited well for arbitrary shape of text in natural images , it often involves several pipelines which leads to inefficiency . [ ] formulates semantic segmentation as a per - pixel classification problem . Following the line of FCN - based semantic segmentation . Dilated convolution is proposed by to enlarge the receptive field . The architecture of our proposed CoupleNet is illustrated in Figure [ reference ] . Our CoupleNet includes two different branches : a ) a local part - sensitive fully convolutional network to learn the object - specific parts , denoted as local FCN ; b ) a global region - sensitive fully convolutional network to encode the whole appearance structure and context prior of the object , denoted as global FCN . We first use the ImageNet pre - trained ResNet - 101 released in to initialize our network . And the effective performance gains brought by deformable ConvNets also hold . By further testing on multiple image scales ( the image shorter side is in [ 480 , 576 , 688 , 864 , 1200 , 1400 ] ) and performing iterative bounding box average , the mAP@ [ 0.5:0.95 ] scores are increased to 37.5 % for the deformable version of R - FCN . Note that the performance gain of deformable ConvNets is complementary to these bells and whistles . In the 3D localization task bounding boxes are projected to bird ’s eye view plane and IoU is evaluated on oriented 2D boxes . Again , our method significantly outperforms previous works which include DoBEM and MV3D that use CNNs on projected LiDAR images , as well as 3D FCN that uses 3D CNNs on voxelized point cloud . The output of our network is visualized in Fig . This makes it a fair comparison in terms of the size of the model . The performance of this FCN variant is poorer than SegNet - Basic in test but also its training accuracy is lower for the same number of training epochs . This shows that using a larger decoder is not enough but it is also important to capture encoder feature map information to learn better , particular the fine grained contour information ( notice the drop in the BF measure ) . The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer . We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving . To address these problems , we propose a new video text tracking approach based on hybrid deep text detection and layout constraint . Firstly , a deep text detection network that combines the advantages of object detection and semantic segmentation in a hybrid way is proposed to locate possible text candidates in individual frames . Then , text trajectories are derived from consecutive frames with a novel data association method , which effectively exploits the layout constraint of text regions in large camera motion . This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection , called DGST ( Discriminator Guided Scene Text detector ) . Instead of binary text score maps generated by some existing semantic segmentation based methods , we generate a multi-scale soft text score map with more information to represent the text position more reasonably , and solve the problem of text pixel adhesion in the process of text extraction . Experiments on standard datasets demonstrate that the proposed DGST brings noticeable gain and outperforms state-of-the-art methods . Formerly , RNN-attention based methods have dominated this field , but suffer from the problem of \textit{attention drift } in certain situations . Lately , semantic segmentation based algorithms have proven effective at recognizing text of different forms ( horizontal , oriented and curved ) . However , these methods may produce spurious characters or miss genuine characters , as they rely heavily on a thresholding procedure operated on segmentation maps . While this framework was shown to lead to high accuracy for image classification , to our best knowledge this is its first application to the segmentation problem . The semantic segmentation process is then guided by a low-level segmentation which enforces local consistency . Finally , to enforce image-level consistency we use global image classifiers : if an image as a whole is unlikely to contain an object class , then the corresponding class is not considered in the segmentation pipeline . Our best results on this collection were achieved by using 45-second long segments with 15-second shifts . Semantic-based segmentation can be divided into three types : similarity-based ( producing segments with high intra-similarity and low inter-similarity ) , lexical-chain-based ( producing segments with frequent lexically connected words ) , and feature-based ( combining various features which signalize a segment break in a machine-learning setting ) . In this work , we mainly focus on feature-based segmentation which allows exploiting various features from all modalities of the data ( including segment length ) in a single trainable model and produces segments which can eventually overlap . Our preliminary results show that even simple semantic-based segmentation outperforms regular segmentation . Our model is a decision tree incorporating the following features : shot segments , output of TextTiling algorithm , cue words ( well , thanks , so , I , now ) , sentence breaks , and the length of the silence after the previous word . It is necessary to adopt more effective methods to detect the tunnel cracks in time . This paper builds the first tunnel crack dataset with semantic segmentation annotation and proposes an objective and fast tunnel crack identification algorithm using semantic segmentation in computer vision to construct a complete tunnel crack identification and analysis system . The system applies advanced semantic segmentation to the railway tunnel image analysis to achieve precise segmentation of tunnel crack locations , thereby saving the railway department a lot of manpower and material resources and improving efficiency . Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots . Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency . Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results . The automatic digitizing of paper maps is a significant and challenging task for both academia and industry . As an important procedure of map digitizing , the semantic segmentation section is mainly relied on manual visual interpretation with low efficiency . In this study , we select urban planning maps as a representative sample and investigate the feasibility of utilizing U-shape fully convolutional based architecture to perform end-to-end map semantic segmentation . Our first baseline is consistent with current trends in the research community . It combines a GAN with a semantic segmentation objective . The generator is trained to synthesize an image that fools the discriminator [ reference ] . For the semantic segmentation network , we use publicly available networks that were pretrained for the Cityscapes dataset [ reference ] and the NYU dataset [ reference ] . The training objective combines the GAN loss and the semantic segmentation ( pixelwise cross - entropy ) loss . Full - resolution network . subsection : Network Overview The aforementioned two modules can be integrated with a conventional semantic segmentation network , such as DilatedNet , DeepLab , PSPNet . In particular , let us denote as the segmentation loss , then our ROAD - Net model can be trained by minimizing a joint loss as follows , where and are two trade - off parameters , which are set to and in our experiments . The resolution of images is around pixels which is similar with Cityscapes , the semantic categories are also compatible between the two datasets . In our experiments , we test the our proposed ROAD - Net with two semantic segmentation methods : DeepLab v2 and PSPNet . However , as discussed in the previous section , our method can be straightforwardly applied to other semantic segmentation methods as well . The curriculum domain adaptation begins with the easy tasks , in order to gain some high - level properties about the unknown pixel - level labels for each target image . It then learns a semantic segmentation network — the hard task , whose predictions over the target images are forced to follow those necessary properties as much as possible . We achieve state - of - the - art results on instancelevel segmentation of both body parts and humans . Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network . Inspired by recent advance on instance - aware semantic segmentation , we present an end - end trainable framework called Fused Text Segmentation Networks ( FTSN ) to handle arbitrary - shape text detection with no extra pipelines involved . It inherits merits from both object detection and semantic segmentation architecture which efficiently detects and segments an text instance simultaneously and accurately gives predictions in the pixel level . As text may rely on finer feature representation , a fused structure formed by multi - level feature maps is set to fit this property . [ reference ] ( b ) ) . Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig . We propose a novel Progressive Scale Expansion Network ( PSENet ) to successfully detect the text instances with arbitrary shapes in natural scene images . By gradually expanding the detected areas from small kernels to large and complete instances via multiple semantic segmentation maps , our method is robust to shapes and can easily distinguish those text instances which are very close or even partially intersected . The experiments on scene text detection benchmarks demonstrate the superior performance of the proposed method . Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory . Unlike previous two-dimensional image segmentation methods , we use 3D U-Nets to obtain the heart cavity directly in 3D . The dual 3D U-Net structure consists of , a first U-Net to coarsely segment and locate the left atrium , and a second U-Net to accurately segment the left atrium under higher resolution . In addition , we introduce a loss function based on additional distance information to adjust the final segmentation . For years the best performing authomatic methods were multi atlas based with around 90\% Dice coefficient and very time consuming , but machine learning methods are recently rising with promising time and accuracy performance . A method for volumetric hippocampus segmentation is presented , based on the consensus of tri-planar U-Net inspired fully convolutional networks ( FCNNs ) , with some modifications , including residual connections , VGG weight transfers , batch normalization and a patch extraction technique employing data from neighbor patches . A study on the impact of our modifications to the classical U-Net architecture was performed . For years the best performing authomatic methods were multi atlas based with around 80 - 85 % Dice coefficient and very time consuming , but machine learning methods are recently rising with promising time and accuracy performance . A method for volumetric hippocampus segmentation is presented , based on the consensus of tri-planar U-Net inspired fully convolutional networks ( FCNNs ) , with some modifications , including residual connections , VGG weight transfers , batch normalization and a patch extraction technique employing data from neighbor patches . A study on the impact of our modifications to the classical U-Net architecture was performed . This is often the case in medical imaging . We also present a novel training strategy for the UNet , leveraging U-Net ’s Few-Shot capability for inter-patient consistent protocols . We propose repeatedly enforcing Few-Sample-Fitting the network for faster convergence . In this study , given the characteristic of GAN to generate missing information , the sinogram-inpainting-GAN ( SI-GAN ) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction . We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images . Furthermore , we propose a joint projection domain and image domain loss function , in which the weighted image domain loss can be added by the back-projection operation . The most common methods for semantic segmentation involve complex two-stage networks or networks which make use of a multi-scale scene parsing module . In this paper , we propose a modified version of the popular U-Net architecture called Squeeze and Excitation U-Net and train it with a loss that helps in directly optimizing the intersection over union ( IoU ) score . Our method gives comparable performance to other methods while having the additional benefit of being computationally efficient . The overall Accuracy , PR-AUC , ROC-AUC and F1-score achieved were 0.98 , 0.95 , 0.89 and 0.88 respectively which are better than most conventional deep net models . The results also showcase the shortcomings of CNN , RDA U-Net and other models and how they can be rectified using the WGAN-RDA-UNET model . In addition , the traditional baselines are endowed with different weights to automatically assign labels . Secondly , the deformable convolution network , through replacing the plain counterparts in the standard U-Net mode , can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins ’ scales and shapes . Moreover , the above features can be further mined and accumulated by combining the recurrent neural network ( RNN ) and the residual network ( ResNet ) . Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy ( HCM ) and 21 healthy control subjects . In terms of segmentation , we compared state-of-the-art CNN frameworks , U-Net and dilated convolution architectures , with and without temporal context , using cross validation with three folds . Performance relative to expert manual segmentation was similar across all networks : pixel accuracy was \(\sim 97\%\ ) , intersection-over-union ( IoU ) across all classes was \(\sim 87\%\ ) , and IoU across foreground classes only was \(\sim 85\%\ ) . To the best of our knowledge , deep learning with semantic segmentation utilizing both visual ( VIS ) and infrared channels ( NIR ) has never been exploited with the HSI data with human tumors . We propose using channels selection with U-Net deep neural network for tumor segmentation in hyperspectral images . The proposed method , based on bigger patches , accounts for bigger spatial context and achieves better results ( average dice coefficient 0.89 ± 0.07 and area under the ROC-curve AUC 0.93 ± 0.04 ) than pixel-level spectral and structural approaches in a clinical data set with tongue squamous cell carcinoma . , scenario 2 planning the LTEAdvanced Pro network uses the LAA method by combining the unlicensed 20 MHz spectrum in band 36 and a licensed 20 MHz sprectrum in band 3 and using a 2x2 MIMO antenna . analysis and simulation using U-Net V500 software . The results of the LTE network planning simulation obtained the average value of RSRP 77 -77.71 dBm , SINR ≥ 11.88 dB , Throughput ≥ 37.079 Mbps and User connected = 98.00 % , while the LTE-AP average value of RSRP ≥ -73.51 dBm , SINR ≥ 17.02 dB , Throughput ≥ 49,739 Mbps and User connected = 100.00 % . Scene text recognition tries to extract text information from natural images , being widely applied in computer vision and intelligent information processing . In this paper , we propose a novel end-to-end approach to scene text recognition with a specially trained fully convolutional network for predicting the centroid and pixel cluster of each character . With the help of this new information , we can solve the character instance segmentation problem effectively and then combine the recognized characters into words to accomplish the text recognition task . We show how to train a fully convolutional neural network to perform inverse rendering from a single , uncontrolled image . The network takes an RGB image as input , regresses albedo and normal maps from which we compute lighting coefficients . We entered the encoding layers i ) with He uniform ( " random " ) initialization , ii ) pretrained ImageNet weights , or iii ) after fine-tuning ResNet34 for skin lesion classification . After transferring the layers to the fully convolutional network architecture we trained for a binary segmentation task using official ISIC 2017 challenge data . RESULTS Pretraining of ResNet34-layers with either ImageNet or fine-tuning for skin lesion classification achieved a higher Jaccard than random initialization ( 0.763 and 0.768 vs 0.740 ) on the ISIC 2017 test-set . To overcome the limited view problem , we present a machine learning-based approach to the reconstruction of initial pressure from limited view PA data . Our method involves a fully convolutional deep neural network based on a U-Net-like architecture with pixel-wise regression loss on the acquired PA images . It is trained and validated on in silico data generated with Monte Carlo simulations . We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect , segment , and analyze retinal vessels . In particular , we propose a novel , fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder . We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions . The first sub-network is a deep network with Siamese branches and 3D convolutional layer , it learns parallax and global information and generates a global depth estimation result in low resolution . The second is a fully convolutional deep network , which reconstructions the depth map to original resolution . The two sub-networks are connected by a pool pyramid . We present a Fully Convolutional Network based method for layout analysis of non-binarized historical Arabic manuscripts . The document image is segmented into main text and side text regions by dense pixel prediction . We theoretically derive the semantic segmentation as an optimization problem parameterized by these links . Finally , the optimization problem enables us to take advantage of state-of-the-art fully convolutional network structure for the implementation of the above encoders and decoder . Experimental results on several representative datasets demonstrate our supreme performance for semantic segmentation . The redundant convolutional encoder-decoder network has been proven useful in speech enhancement tasks . This network can capture the localized time-frequency details of speech signals through the fully convolutional network structure and the feature selection capability that results from the encoder-decoder mechanism . However , extracting informative features , which we regard as important for the representational capability of speech enhancement models , is not considered explicitly . Compared with traditional methods , the segmentation algorithms based on fully convolutional neural network greatly improve the accuracy of semantic segmentation . For the problem of left ventricular segmentation , a new fully convolutional neural network structure named MS-FCN is proposed in this paper . The MS-FCN network employs a multi-scale pooling module to ensure that the network maximises the feature extraction ability and uses a dense connectivity decoder to refine the boundaries of the object . This paper introduces a new method for multi-channel time domain speech separation in reverberant environments . A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings , with no need of conventional spatial feature extraction . To reduce the influence of reverberation on spatial feature extraction , a dereverberation pre-processing method has been applied to further improve the separation performance . Comparisons with VDSR [ ] and DRCN [ ] Concurrent to our work , networks which incorporate residual learning for image super - resolution are proposed . In , a fully convolutional network termed VDSR is proposed to learn the residual image for image super - resolution . The loss layer takes three inputs : residual estimate , low - resolution input and ground truth high - resolution image , and Euclidean loss is computed between the reconstructed image ( the sum of network input and output ) and ground truth . Reusing max - pooling indices in the decoding process has several practical advantages ; ( i ) it improves boundary delineation , ( ii ) it reduces the number of parameters enabling end - to - end training , and ( iii ) this form of upsampling can be incorporated into any encoder - decoder architecture such as with only a little modification . One of the main contributions of this paper is our analysis of the SegNet decoding technique and the widely used Fully Convolutional Network ( FCN ) . This is in order to convey the practical trade - offs involved in designing segmentation architectures . State - of - the - art semantic segmentation methods are mainly based on the recent advances of deep neural networks . As proposed by Long , one can transform a classification CNN ( e.g. , AlexNet , VGG , or ResNet ) to a fully - convolutional network ( FCN ) for semantic segmentation . Numerous methods have since been developed to improve this model by utilizing context information or enlarging receptive fields . The key component of the proposed algorithm is a neural network model , which is trained to directly predict the existence of text instances and their geometries from full images . The model is a fully - convolutional neural network adapted for text detection that outputs dense per - pixel predictions of words or text lines . This eliminates intermediate steps such as candidate proposal , text region formation and word partition . Semantic segmentation , largely studied in the last 10 years , merges image segmentation with object recognition to produce per - pixel labeling of image content . The currently most successful techniques for semantic segmentation are based on fully convolution networks ( FCN ) . These are adapted from networks designed to classify whole images , and have demonstrated impressive level of performance . Yet , these methods still fall short of including whole image context and only classify patches or pixels locally . Our ParseNet is built upon the fully convolutional network architecture with a strong emphasis on including contextual information in a simple approach . For semantic segmentation , using context information from the whole image can significantly help classifying local patches . section : Conclusion In this work we presented ParseNet , a simple fully convolutional neural network architecture that allows for direct inclusion of global context for the task of semantic segmentation . We have explicitly demonstrated that relying on the largest receptive field of FCN network does not provide sufficient global context , and the largest empirical receptive field is not sufficient to capture global context – modeling global context directly in required . We now turn to the task of training a deep network that predicts dense correspondences between image pixels and surface points . Such a task was recently addressed in the Dense Regression ( DenseReg ) system of through a fully - convolutional network architecture . In this work , we introduce improved architectures by combining the DenseReg approach with the Mask - RCNN architecture , yielding our ‘ DensePose - RCNN ’ system . Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] . Particularly , DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks . We propose HyperDenseNet , a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems . Each imaging modality has a path , and dense connections occur not only between the pairs of layers within the same path but also between those across different paths .
Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting . The proposed Fully Convolutional Network ( FCN ) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive . The global average pooling in our convolutional model enables the exploitation of the Class Activation Map ( CAM ) to find out the contributing region in the raw data for the specific labels .
Furthermore , based on the prediction results , the post-processing methods of 3D morphological closed operation and volume analysis are employed to smooth the surface of vessels and eliminate noise blocks , respectively . Compared with existing 3D DenseNet , FCN and 3D U-Net , the average Dice coefficients of our method in hepatic veins and portal veins segmentation are 71.7 % and 76.5 % respectively , which are superior to 55.3 % and 53.9 % of the 3D DenseNet , 60.2 % and 75.6 % of the FCN , and 66.4 % and 73.9 % of the 3D U-Net . Meanwhile , the cross validation results prove that our method is accurate and stable for liver vessel extraction .
However , various nonidealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks , and hence , impose restrictions on feasible crossbar sizes . In this paper , we build mathematical models of various nonidealities that occur in crossbar implementations such as source resistance , neuron resistance , and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network ( FCN ) and convolutional neural network ( CNN ) trained with Backpropagation algorithm . We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84 % for FCNs and 62.4 % for CNNs when implemented on nonideal crossbars for relevant nonideality ranges .
In this paper , we propose a simple but effective framework to automatically extract accurate boundaries of synaptic cleft regions . Our approach concatenates a novel contour growing algorithm to a fully convolutional network ( FCN ) , so that it takes both advantages of large receptive field of FCNs and fine-level localization of contour evolution . The contour growing algorithm is based on the flexible evolving tension and synchronous growing controlling to localize the opening contour of clef region .
Therefore , how to dig out valuable information in these images and help doctors to make diagnosis more accurately and quickly have always been the concern of researchers . In this paper , we have made some improvements to the FCN network and incorporated Inception Architecture into it to build several convolutional neural networks . In our experiments , we trained the networks in IBSR dataset and contrasted the results with some classical methods .
Secondly , the mathematical morphology reconstruction operation is used to improve the segmentation result to get the accurate localization of the insulator areas . Finally , the FCN network is used again to detect the insulator fault and obtain the fault region . Experiments show that the proposed SOFCN is not only able to obtain accurate insulator region , but also able to effectively suppress the interference of noninsulator region .
Secondly , focal loss was applied to make the training focus on wrongly-classified pixels and hence achieve good performance on small object segmentation . Comparisons between FCN , Weighted FCN , Fully-weighted FCN and Focal FCN were tested on customized stent graft marker segmentation .
Motivated by this observation that accurately salient object detection is related to both foreground and background , we proposed a novel cooperative attention mechanism that jointly considers reciprocal relationships between background and foreground for efficient salient object detection . Concretely , we first aggregate the features at each side-out of traditional dilated FCN to extract the initial foreground and background local responses respectively . Then taking these responses as input , reciprocal attention module adaptively models the nonlocal dependencies between any two pixels of the foreground and background features , which is then aggregated with local features in a mutual reinforced way so as to enhance each branch to generate more discriminative foreground and background saliency map .
In FPGA , an encoder – decoder-based FCN is utilized to consider the global spatial information by processing the whole image , which results in fast inference . However , it is difficult to directly utilize the encoder – decoder-based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples . To solve the divergence problem and maintain the FCNs abilities of fast inference and global spatial information mining , a global stochastic stratified ( GS2 ) sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples .
Large working data sets result in heavy accesses across the memory hierarchy , moreover large amounts of intermediate data are also generated due to the large number of multiply-and-accumulate ( MAC ) operations for both CNN and FCN . Even when binary-based DNN [ 3 ] are used , the required CNN and FCN operations result in a major memory I/O bottleneck for AI edge devices .
Each level of the FCN cascade is a multi-scale fully-convolutional network , which generates scores at different locations and in different scales . A score map is generated after each FCN stage . Probable regions of face are selected and fed to the next stage .
Processing in the sinogram domain directly can make the effective information maximum extent preserved . In this paper , we propose a novel method based on full convolutional network ( FCN ) for metal artifact reduction in the sinogram domain . The networks we introduced use the complete sinogram data to learn a mapping function to correct the metal-corrupted sinogram data .
Next , we demonstrate improved accuracy on PASCAL VOC 2011 - 2 , NYUDv2 , SIFT Flow , and PASCAL - Context . Finally , we analyze design choices , examine what cues can be learned by an FCN , and calculate recognition bounds for semantic segmentation . section : Related Work
A number of recent approaches improve detection and segmentation by using different layers in a ConvNet . FCN [ reference ] sums partial scores for each category over multiple scales to compute semantic segmentations . Hypercolumns [ reference ] uses a similar method for object instance segmentation .
subsubsection : High Quality Semantic Segmentation FCN is the pioneer work to replace the last fully - connected layers in classification with convolution layers . DeepLab and used dilated convolution to enlarge the receptive field for dense labeling .
We encode both visual and contextual information to represent a superpixel . First , we use the FCN - 8s pre - trained on the PASCAL CONTEXT dataset , which has 59 distinct classes , to obtain 59 detection scores for each pixel . We then average them within each superpixel .
In the latter case , it improves per - pixel accuracy from 54 % to 82 % , nearly closing the gap to the target - trained model . Our experiments confirm that domain adaptation can benefit greatly from cycle - consistent pixel transformations , and that this is especially important for pixel - level semantic segmentation with contemporary FCN architectures . Further , we show that adaptation at both the pixel and representation level can offer complementary improvements with joint pixel - space and feature adaptation leading to the highest performing model for digit classification tasks .
However , they may also be restricted to rectangular bounding box constraints even with appropriate rotation . Different from these methods , FCN based approaches generate text / non - text map which classifies text at the pixel level . Though it may be suited well for arbitrary shape of text in natural images , it often involves several pipelines which leads to inefficiency .
[ ] formulates semantic segmentation as a per - pixel classification problem . Following the line of FCN - based semantic segmentation . Dilated convolution is proposed by to enlarge the receptive field .
The architecture of our proposed CoupleNet is illustrated in Figure [ reference ] . Our CoupleNet includes two different branches : a ) a local part - sensitive fully convolutional network to learn the object - specific parts , denoted as local FCN ; b ) a global region - sensitive fully convolutional network to encode the whole appearance structure and context prior of the object , denoted as global FCN . We first use the ImageNet pre - trained ResNet - 101 released in to initialize our network .
And the effective performance gains brought by deformable ConvNets also hold . By further testing on multiple image scales ( the image shorter side is in [ 480 , 576 , 688 , 864 , 1200 , 1400 ] ) and performing iterative bounding box average , the mAP@ [ 0.5:0.95 ] scores are increased to 37.5 % for the deformable version of R - FCN . Note that the performance gain of deformable ConvNets is complementary to these bells and whistles .
In the 3D localization task bounding boxes are projected to bird ’s eye view plane and IoU is evaluated on oriented 2D boxes . Again , our method significantly outperforms previous works which include DoBEM and MV3D that use CNNs on projected LiDAR images , as well as 3D FCN that uses 3D CNNs on voxelized point cloud . The output of our network is visualized in Fig .
This makes it a fair comparison in terms of the size of the model . The performance of this FCN variant is poorer than SegNet - Basic in test but also its training accuracy is lower for the same number of training epochs . This shows that using a larger decoder is not enough but it is also important to capture encoder feature map information to learn better , particular the fine grained contour information ( notice the drop in the BF measure ) .
The motivation of this paper is to systematically summarize efficient design techniques and provide guidelines for an application developer . We also perform a case study by benchmarking various semantic segmentation algorithms for autonomous driving .
To address these problems , we propose a new video text tracking approach based on hybrid deep text detection and layout constraint . Firstly , a deep text detection network that combines the advantages of object detection and semantic segmentation in a hybrid way is proposed to locate possible text candidates in individual frames . Then , text trajectories are derived from consecutive frames with a novel data association method , which effectively exploits the layout constraint of text regions in large camera motion .
This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection , called DGST ( Discriminator Guided Scene Text detector ) . Instead of binary text score maps generated by some existing semantic segmentation based methods , we generate a multi-scale soft text score map with more information to represent the text position more reasonably , and solve the problem of text pixel adhesion in the process of text extraction . Experiments on standard datasets demonstrate that the proposed DGST brings noticeable gain and outperforms state-of-the-art methods .
Formerly , RNN-attention based methods have dominated this field , but suffer from the problem of \textit{attention drift } in certain situations . Lately , semantic segmentation based algorithms have proven effective at recognizing text of different forms ( horizontal , oriented and curved ) . However , these methods may produce spurious characters or miss genuine characters , as they rely heavily on a thresholding procedure operated on segmentation maps .
While this framework was shown to lead to high accuracy for image classification , to our best knowledge this is its first application to the segmentation problem . The semantic segmentation process is then guided by a low-level segmentation which enforces local consistency . Finally , to enforce image-level consistency we use global image classifiers : if an image as a whole is unlikely to contain an object class , then the corresponding class is not considered in the segmentation pipeline .
Our best results on this collection were achieved by using 45-second long segments with 15-second shifts . Semantic-based segmentation can be divided into three types : similarity-based ( producing segments with high intra-similarity and low inter-similarity ) , lexical-chain-based ( producing segments with frequent lexically connected words ) , and feature-based ( combining various features which signalize a segment break in a machine-learning setting ) . In this work , we mainly focus on feature-based segmentation which allows exploiting various features from all modalities of the data ( including segment length ) in a single trainable model and produces segments which can eventually overlap . Our preliminary results show that even simple semantic-based segmentation outperforms regular segmentation . Our model is a decision tree incorporating the following features : shot segments , output of TextTiling algorithm , cue words ( well , thanks , so , I , now ) , sentence breaks , and the length of the silence after the previous word .
It is necessary to adopt more effective methods to detect the tunnel cracks in time . This paper builds the first tunnel crack dataset with semantic segmentation annotation and proposes an objective and fast tunnel crack identification algorithm using semantic segmentation in computer vision to construct a complete tunnel crack identification and analysis system . The system applies advanced semantic segmentation to the railway tunnel image analysis to achieve precise segmentation of tunnel crack locations , thereby saving the railway department a lot of manpower and material resources and improving efficiency .
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots . Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency . Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results .
The automatic digitizing of paper maps is a significant and challenging task for both academia and industry . As an important procedure of map digitizing , the semantic segmentation section is mainly relied on manual visual interpretation with low efficiency . In this study , we select urban planning maps as a representative sample and investigate the feasibility of utilizing U-shape fully convolutional based architecture to perform end-to-end map semantic segmentation .
Our first baseline is consistent with current trends in the research community . It combines a GAN with a semantic segmentation objective . The generator is trained to synthesize an image that fools the discriminator [ reference ] .
For the semantic segmentation network , we use publicly available networks that were pretrained for the Cityscapes dataset [ reference ] and the NYU dataset [ reference ] . The training objective combines the GAN loss and the semantic segmentation ( pixelwise cross - entropy ) loss . Full - resolution network .
subsection : Network Overview The aforementioned two modules can be integrated with a conventional semantic segmentation network , such as DilatedNet , DeepLab , PSPNet . In particular , let us denote as the segmentation loss , then our ROAD - Net model can be trained by minimizing a joint loss as follows , where and are two trade - off parameters , which are set to and in our experiments .
The resolution of images is around pixels which is similar with Cityscapes , the semantic categories are also compatible between the two datasets . In our experiments , we test the our proposed ROAD - Net with two semantic segmentation methods : DeepLab v2 and PSPNet . However , as discussed in the previous section , our method can be straightforwardly applied to other semantic segmentation methods as well .
The curriculum domain adaptation begins with the easy tasks , in order to gain some high - level properties about the unknown pixel - level labels for each target image . It then learns a semantic segmentation network — the hard task , whose predictions over the target images are forced to follow those necessary properties as much as possible .
We achieve state - of - the - art results on instancelevel segmentation of both body parts and humans . Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network .
Inspired by recent advance on instance - aware semantic segmentation , we present an end - end trainable framework called Fused Text Segmentation Networks ( FTSN ) to handle arbitrary - shape text detection with no extra pipelines involved . It inherits merits from both object detection and semantic segmentation architecture which efficiently detects and segments an text instance simultaneously and accurately gives predictions in the pixel level . As text may rely on finer feature representation , a fused structure formed by multi - level feature maps is set to fit this property .
[ reference ] ( b ) ) . Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig .
We propose a novel Progressive Scale Expansion Network ( PSENet ) to successfully detect the text instances with arbitrary shapes in natural scene images . By gradually expanding the detected areas from small kernels to large and complete instances via multiple semantic segmentation maps , our method is robust to shapes and can easily distinguish those text instances which are very close or even partially intersected . The experiments on scene text detection benchmarks demonstrate the superior performance of the proposed method .
Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory .
Unlike previous two-dimensional image segmentation methods , we use 3D U-Nets to obtain the heart cavity directly in 3D . The dual 3D U-Net structure consists of , a first U-Net to coarsely segment and locate the left atrium , and a second U-Net to accurately segment the left atrium under higher resolution . In addition , we introduce a loss function based on additional distance information to adjust the final segmentation .
For years the best performing authomatic methods were multi atlas based with around 90\% Dice coefficient and very time consuming , but machine learning methods are recently rising with promising time and accuracy performance . A method for volumetric hippocampus segmentation is presented , based on the consensus of tri-planar U-Net inspired fully convolutional networks ( FCNNs ) , with some modifications , including residual connections , VGG weight transfers , batch normalization and a patch extraction technique employing data from neighbor patches . A study on the impact of our modifications to the classical U-Net architecture was performed .
For years the best performing authomatic methods were multi atlas based with around 80 - 85 % Dice coefficient and very time consuming , but machine learning methods are recently rising with promising time and accuracy performance . A method for volumetric hippocampus segmentation is presented , based on the consensus of tri-planar U-Net inspired fully convolutional networks ( FCNNs ) , with some modifications , including residual connections , VGG weight transfers , batch normalization and a patch extraction technique employing data from neighbor patches . A study on the impact of our modifications to the classical U-Net architecture was performed .
This is often the case in medical imaging . We also present a novel training strategy for the UNet , leveraging U-Net ’s Few-Shot capability for inter-patient consistent protocols . We propose repeatedly enforcing Few-Sample-Fitting the network for faster convergence .
In this study , given the characteristic of GAN to generate missing information , the sinogram-inpainting-GAN ( SI-GAN ) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction . We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images . Furthermore , we propose a joint projection domain and image domain loss function , in which the weighted image domain loss can be added by the back-projection operation .
The most common methods for semantic segmentation involve complex two-stage networks or networks which make use of a multi-scale scene parsing module . In this paper , we propose a modified version of the popular U-Net architecture called Squeeze and Excitation U-Net and train it with a loss that helps in directly optimizing the intersection over union ( IoU ) score . Our method gives comparable performance to other methods while having the additional benefit of being computationally efficient .
The overall Accuracy , PR-AUC , ROC-AUC and F1-score achieved were 0.98 , 0.95 , 0.89 and 0.88 respectively which are better than most conventional deep net models . The results also showcase the shortcomings of CNN , RDA U-Net and other models and how they can be rectified using the WGAN-RDA-UNET model .
In addition , the traditional baselines are endowed with different weights to automatically assign labels . Secondly , the deformable convolution network , through replacing the plain counterparts in the standard U-Net mode , can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins ’ scales and shapes . Moreover , the above features can be further mined and accumulated by combining the recurrent neural network ( RNN ) and the residual network ( ResNet ) .
Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy ( HCM ) and 21 healthy control subjects . In terms of segmentation , we compared state-of-the-art CNN frameworks , U-Net and dilated convolution architectures , with and without temporal context , using cross validation with three folds . Performance relative to expert manual segmentation was similar across all networks : pixel accuracy was \(\sim 97\%\ ) , intersection-over-union ( IoU ) across all classes was \(\sim 87\%\ ) , and IoU across foreground classes only was \(\sim 85\%\ ) .
To the best of our knowledge , deep learning with semantic segmentation utilizing both visual ( VIS ) and infrared channels ( NIR ) has never been exploited with the HSI data with human tumors . We propose using channels selection with U-Net deep neural network for tumor segmentation in hyperspectral images . The proposed method , based on bigger patches , accounts for bigger spatial context and achieves better results ( average dice coefficient 0.89 ± 0.07 and area under the ROC-curve AUC 0.93 ± 0.04 ) than pixel-level spectral and structural approaches in a clinical data set with tongue squamous cell carcinoma .
, scenario 2 planning the LTEAdvanced Pro network uses the LAA method by combining the unlicensed 20 MHz spectrum in band 36 and a licensed 20 MHz sprectrum in band 3 and using a 2x2 MIMO antenna . analysis and simulation using U-Net V500 software . The results of the LTE network planning simulation obtained the average value of RSRP 77 -77.71 dBm , SINR ≥ 11.88 dB , Throughput ≥ 37.079 Mbps and User connected = 98.00 % , while the LTE-AP average value of RSRP ≥ -73.51 dBm , SINR ≥ 17.02 dB , Throughput ≥ 49,739 Mbps and User connected = 100.00 % .
Scene text recognition tries to extract text information from natural images , being widely applied in computer vision and intelligent information processing . In this paper , we propose a novel end-to-end approach to scene text recognition with a specially trained fully convolutional network for predicting the centroid and pixel cluster of each character . With the help of this new information , we can solve the character instance segmentation problem effectively and then combine the recognized characters into words to accomplish the text recognition task .
We show how to train a fully convolutional neural network to perform inverse rendering from a single , uncontrolled image . The network takes an RGB image as input , regresses albedo and normal maps from which we compute lighting coefficients .
We entered the encoding layers i ) with He uniform ( " random " ) initialization , ii ) pretrained ImageNet weights , or iii ) after fine-tuning ResNet34 for skin lesion classification . After transferring the layers to the fully convolutional network architecture we trained for a binary segmentation task using official ISIC 2017 challenge data . RESULTS Pretraining of ResNet34-layers with either ImageNet or fine-tuning for skin lesion classification achieved a higher Jaccard than random initialization ( 0.763 and 0.768 vs 0.740 ) on the ISIC 2017 test-set .
To overcome the limited view problem , we present a machine learning-based approach to the reconstruction of initial pressure from limited view PA data . Our method involves a fully convolutional deep neural network based on a U-Net-like architecture with pixel-wise regression loss on the acquired PA images . It is trained and validated on in silico data generated with Monte Carlo simulations .
We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect , segment , and analyze retinal vessels . In particular , we propose a novel , fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the encoder and add multiscale contextual information to the decoder . We also propose a simple yet effective way of quantifying and tracking the widths of retinal vessels through direct use of the segmentation predictions .
The first sub-network is a deep network with Siamese branches and 3D convolutional layer , it learns parallax and global information and generates a global depth estimation result in low resolution . The second is a fully convolutional deep network , which reconstructions the depth map to original resolution . The two sub-networks are connected by a pool pyramid .
We present a Fully Convolutional Network based method for layout analysis of non-binarized historical Arabic manuscripts . The document image is segmented into main text and side text regions by dense pixel prediction .
We theoretically derive the semantic segmentation as an optimization problem parameterized by these links . Finally , the optimization problem enables us to take advantage of state-of-the-art fully convolutional network structure for the implementation of the above encoders and decoder . Experimental results on several representative datasets demonstrate our supreme performance for semantic segmentation .
The redundant convolutional encoder-decoder network has been proven useful in speech enhancement tasks . This network can capture the localized time-frequency details of speech signals through the fully convolutional network structure and the feature selection capability that results from the encoder-decoder mechanism . However , extracting informative features , which we regard as important for the representational capability of speech enhancement models , is not considered explicitly .
Compared with traditional methods , the segmentation algorithms based on fully convolutional neural network greatly improve the accuracy of semantic segmentation . For the problem of left ventricular segmentation , a new fully convolutional neural network structure named MS-FCN is proposed in this paper . The MS-FCN network employs a multi-scale pooling module to ensure that the network maximises the feature extraction ability and uses a dense connectivity decoder to refine the boundaries of the object .
This paper introduces a new method for multi-channel time domain speech separation in reverberant environments . A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings , with no need of conventional spatial feature extraction . To reduce the influence of reverberation on spatial feature extraction , a dereverberation pre-processing method has been applied to further improve the separation performance .
Comparisons with VDSR [ ] and DRCN [ ] Concurrent to our work , networks which incorporate residual learning for image super - resolution are proposed . In , a fully convolutional network termed VDSR is proposed to learn the residual image for image super - resolution . The loss layer takes three inputs : residual estimate , low - resolution input and ground truth high - resolution image , and Euclidean loss is computed between the reconstructed image ( the sum of network input and output ) and ground truth .
Reusing max - pooling indices in the decoding process has several practical advantages ; ( i ) it improves boundary delineation , ( ii ) it reduces the number of parameters enabling end - to - end training , and ( iii ) this form of upsampling can be incorporated into any encoder - decoder architecture such as with only a little modification . One of the main contributions of this paper is our analysis of the SegNet decoding technique and the widely used Fully Convolutional Network ( FCN ) . This is in order to convey the practical trade - offs involved in designing segmentation architectures .
State - of - the - art semantic segmentation methods are mainly based on the recent advances of deep neural networks . As proposed by Long , one can transform a classification CNN ( e.g. , AlexNet , VGG , or ResNet ) to a fully - convolutional network ( FCN ) for semantic segmentation . Numerous methods have since been developed to improve this model by utilizing context information or enlarging receptive fields .
The key component of the proposed algorithm is a neural network model , which is trained to directly predict the existence of text instances and their geometries from full images . The model is a fully - convolutional neural network adapted for text detection that outputs dense per - pixel predictions of words or text lines . This eliminates intermediate steps such as candidate proposal , text region formation and word partition .
Semantic segmentation , largely studied in the last 10 years , merges image segmentation with object recognition to produce per - pixel labeling of image content . The currently most successful techniques for semantic segmentation are based on fully convolution networks ( FCN ) . These are adapted from networks designed to classify whole images , and have demonstrated impressive level of performance .
Yet , these methods still fall short of including whole image context and only classify patches or pixels locally . Our ParseNet is built upon the fully convolutional network architecture with a strong emphasis on including contextual information in a simple approach . For semantic segmentation , using context information from the whole image can significantly help classifying local patches .
section : Conclusion In this work we presented ParseNet , a simple fully convolutional neural network architecture that allows for direct inclusion of global context for the task of semantic segmentation . We have explicitly demonstrated that relying on the largest receptive field of FCN network does not provide sufficient global context , and the largest empirical receptive field is not sufficient to capture global context – modeling global context directly in required .
We now turn to the task of training a deep network that predicts dense correspondences between image pixels and surface points . Such a task was recently addressed in the Dense Regression ( DenseReg ) system of through a fully - convolutional network architecture . In this work , we introduce improved architectures by combining the DenseReg approach with the Mask - RCNN architecture , yielding our ‘ DensePose - RCNN ’ system .
Alternatively , small architecturally variant models are used for semantic image segmentation tasks . For example , a fully - connected convolutional neural network ( FCN ) also provides state - of - the - art results for image segmentation tasks in computer vision [ reference ] .
Particularly , DenseNet that connects each layer to every other layer in a feed-forward fashion and has shown impressive performances in natural image classification tasks . We propose HyperDenseNet , a 3-D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems . Each imaging modality has a path , and dense connections occur not only between the pairs of layers within the same path but also between those across different paths .
Time series classification from scratch with deep neural networks: A strong baseline Liver Vessels Segmentation Based on 3d Residual U-NET Technology Aware Training in Memristive Neuromorphic Systems for Nonideal Synaptic Crossbars Accurate Segmentation of Synaptic Cleft with Contour Growing Concatenated with a Convnet Brain tissue segmentation based on convolutional neural networks Fault Detection of Insulators Using Second-order Fully Convolutional Network Model Focal FCN: Towards Small Object Segmentation with Limited Training Data Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors A multi-scale cascade fully convolutional network face detector Fully convolutional networks based sinogram correction for metal artifact reduction Fully Convolutional Networks for Semantic Segmentation Feature Pyramid Networks for Object Detection ICNet for Real-Time Semantic Segmentation on High-Resolution Images Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes CyCADA: Cycle-Consistent Adversarial Domain Adaptation Fused Text Segmentation Networks for Multi-oriented Scene Text Detection ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes CoupleNet: Coupling Global Structure with Local Parts for Object Detection Deformable Convolutional Networks Frustum PointNets for 3D Object Detection from RGB-D Data SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Analysis of Efficient CNN Design Techniques for Semantic Segmentation Scene video text tracking based on hybrid deep text detection and layout constraint DGST : Discriminator Guided Scene Text detector TextScanner: Reading Characters in Order for Robust Scene Text Recognition An Efficient Approach to Semantic Segmentation Segmentation strategies for passage retrieval in audio-visual documents Real-Time Tunnel Crack Analysis System via Deep Learning Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation Semantic Segmentation for Urban Planning Maps Based on U-Net Photographic Image Synthesis with Cascaded Refinement Networks ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Holistic, Instance-Level Human Parsing Fused Text Segmentation Networks for Multi-oriented Scene Text Detection Shape Robust Text Detection With Progressive Scale Expansion Network CyCADA: Cycle-Consistent Adversarial Domain Adaptation Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss Extended 2D Volumetric Consensus Hippocampus Segmentation Extended 2D Consensus Hippocampus Segmentation. U-Net in Constraint Few-Shot Settings Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging Segmenting Ships in Satellite Imagery With Squeeze and Excitation U-Net RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks Finger Vein Verification Algorithm Based on Fully Convolutional Neural Network and Conditional Random Field Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization Tumor Semantic Segmentation in Hyperspectral Images using Deep Learning Planning of LTE-Apro Network using Licensed Assisted Access combining Spectrum Licensed 1800MHz and Unlicensed 5GHz End-to-End Scene Text Recognition with Character Centroid Prediction InverseRenderNet: Learning Single Image Inverse Rendering Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation Reconstruction of initial pressure from limited view photoacoustic images using deep learning Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels Binocular Depth Estimation Using Convolutional Neural Network With Siamese Branches Binarization Free Layout Analysis for Arabic Historical Documents Using Fully Convolutional Networks Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation Combining Multi-Perspective Attention Mechanism With Convolutional Networks for Monaural Speech Enhancement Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Learning to Adapt Structured Output Space for Semantic Segmentation EAST: An Efficient and Accurate Scene Text Detector ParseNet: Looking Wider to See Better DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
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IJCNN ICIP ICIP CISP-BMEI ISSCC ICPR IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2017 IEEE International Conference on Computer Vision (ICCV) ICML 2018 24th International Conference on Pattern Recognition (ICPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence CVPR Workshops SIGIR IGARSS 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) BMVC 2018 24th International Conference on Pattern Recognition (ICPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICML Bildverarbeitung f&#252;r die Medizin ICONIP (3) ROBIO ASAR ICASSP ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv
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