For unsupervised domain adaptation problems , the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification , but usually fails in semantic segmentation tasks in which the latent representations are overcomplex . In this work , we equip the adversarial network with a " significance-aware information bottleneck ( SIB ) " , to address the above problem . Targeted on maize , we proposed to jointly segment crop and maize tassel . The task is consequently formulated as a semantic segmentation problem . We proposed a region-based approach that leverages the efficient graph-based segmentation algorithm and simple linear iterative clustering ( SLIC ) to generate region proposals . However , vast majority of the existing methods detect text within local regions , typically through extracting character , word or line level candidates followed by candidate aggregation and false positive elimination , which potentially exclude the effect of wide-scope and long-range contextual cues in the scene . To take full advantage of the rich information available in the whole natural image , we propose to localize text in a holistic manner , by casting scene text detection as a semantic segmentation problem . The proposed algorithm directly runs on full images and produces global , pixel-wise prediction maps , in which detections are subsequently formed . The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene , targeting different clinical needs . Together with the dataset and taking advantage of advances in semantic segmentation literature , we provide new baselines by training standard fully convolutional networks ( FCNs ) . We perform a comparative study to show that FCNs significantly outperform , without any further postprocessing , prior results in endoluminal scene segmentation , especially with respect to polyp segmentation and localization . The goal of this work is to densely predict a comparatively large set of affordances given only single RGB images . We approach this task by using a convolutional neural network based on the well-known ResNet architecture , which we blend with refinement modules recently proposed in the semantic segmentation literature . A novel cost function , capable of handling incomplete data , is introduced , which is necessary because we make use of segmentations of objects and their parts to generate affordance maps . Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available , however with the advancement in the computing processor , the progression is on developing the deep learning application replacing traditional methods . This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose . The proposed model for outdoor scene segmentation is based on the frustum pointnet , and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level . We also discuss the common challenges of the current research , and present several valuable growing research points in this field . This survey is expected to familiarize readers with the progress and challenges of semantic segmentation research in the deep learning era . To achieve this goal , our system connects two separate subsystems together , namely semantic segmentation and portrait synthesis . In the semantic segmentation phase , photographs are segmented into background , hair and skin regions using multiple segmentations method . In the portrait synthesis phase , we treat different regions with different strategies . However , the use of CRF may cause fragmentation effect around object boundaries . In this paper , we propose the use of a so-called iterative contraction and merging ( ICM ) process to facilitate the semantic segmentation process . Guided by the high-level information from CNN , the ICM process is used as a tool to grow image segments in a bottom-up way and to produce more accurate outcomes in an iterative way . Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D . In the semantic segmentation step , the labels on the mesh are updated with MRF inference , such that they are compatible with the semantic segmentations in the input images . Also , this step includes prior assumptions about the surface shape of different semantic classes . Extracting essential contextual information of objects requires CNN models to process features at various scales for effective semantic segmentation . Long et al . proposes one of the earliest methods that enables multi - scale representations of the fully convolutional network ( FCN ) for semantic segmentation task . In DeepLab , Chen To use the ResNet - 101 model pre - trained on JFT - 300 M data , we split the model into two parts : the first part starts from conv1 block and ends at conv4 block , it is used for feature extraction and is shared by both RPN and box classifier ; the second part consists of the conv5 block , it is used by box classifier . Semantic Segmentation . We use the DeepLab framework with ResNet - 101 base architecture for the task of semantic segmentation . section : Abstract Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole . This is different from our work in at least two ways . Firstly , we treat our reconstruction as a semantic segmentation problem by regressing a volume which is spatially aligned with the image . Secondly , we work from only one image in one single step , regressing a much larger volume of as opposed to the used in . Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection . subsection : Volumetric Regression Networks In this section we review approaches that make use of deep learning and CNNs for low - level computer vision tasks , with a focus on semantic image segmentation . A wide variety of approaches have been proposed to tackle the semantic image segmentation task using deep learning . These approaches can be categorized into two main strategies . document : SegNet : A Deep Convolutional Encoder - Decoder Architecture for Image Segmentation We present a novel and practical deep fully convolutional neural network architecture for semantic pixel - wise segmentation termed SegNet . This core trainable segmentation engine consists of an encoder network , a corresponding decoder network followed by a pixel - wise classification layer . We also provide a Caffe implementation of SegNet and a web demo at . Deep Convolutional Neural Networks , Semantic Pixel - Wise Segmentation , Indoor Scenes , Road Scenes , Encoder , Decoder , Pooling , Upsampling . section : Introduction section : Literature Review Semantic pixel - wise segmentation is an active topic of research , fuelled by challenging datasets . Before the arrival of deep networks , the best performing methods mostly relied on hand engineered features classifying pixels independently . We add a layer skip from input to the reconstruction net . Adding layer skips is successfully used for a semantic segmentation network and we employ a similar idea . Now input image is directly fed into the reconstruction net whenever it is used during recursions . paragraph : Experiment setup . Since our ultimate goal is to solve the semantic segmentation problem for real images of urban scenes , we take Cityscapes as the target domain and SYNTHIA as the source domain . The Cityscapes validation set is used as our test set . [ reference ] . The instance embedding provides important details that resolve both inter - and intra - class instance overlap which are not emphasized in the semantic segmentation loss . section : Conclusion and Future Work These include keypoint matching between 2D images and their corresponding 3D CAD models , or aligning 3D - reconstructed models with ground - truth models to recover the object poses . Gupta propose to predict a semantic segmentation map as well as object pose hypotheses using a CNN and then align the hypotheses with known object CAD models using ICP . These types of methods rely on strong category shape priors or ground - truth object CAD models , which makes them difficult to scale to larger datasets . Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies , including cancers . However , such approaches are laborious , time consuming and contain a risk for bias or human error . Nuclei segmentation is a fundamental task in histopathological image analysis . Typically , such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training . To date , the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them . The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net . Using this approach allows to get better results in the tasks of medical image segmentation . Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types . We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches , of every tissue type . Although our synthetic patches are not always of high quality , we harness the motley crew of generated samples through a generally applicable importance sampling method . Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice . State of the art methods relying on hand-crafted annotations that reduce the scope of the solutions since digital histology suffers from standardization and samples differ significantly between cancer phenotypes . In this paper we present a multiphase level set model for histology image segmentation . Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods . This paper briefly describes a few methods for segmentation of such images like images of tissues . We analyze some traditional segmentation techniques for histology image segmentation and relate them with the recent framework that performs well on histology dataset . We describe the methods which are helpful for diagnosing cancer and tumors by looking for malignant tissues . The method is evaluated using two different datasets . Results indicate that accurate histologic segmentation is achievable by this approach . The labelling of e.g. 20 images per slide is surely less time consuming than the validation of a complete whole slide image processed with a fully automatic , but less reliable , segmentation approach . Finally , it should be highlighted that the proposed interaction protocol could easily be adapted to other histopathological classification or segmentation tasks , also for implementation in a clinical system . Additionally , some parameter adaptation techniques are also incorporated in the proposed methodology . The modified FPA has been successfully applied for histopathological image segmentation problem . The experimental and computational effort results indicate its effectiveness over existing swarm intelligence algorithms and machine learning methods . Complex laboratory investigation is necessary for the diagnosis and relevant classification of lymphomas . The classical histopathological morphology and cytology investigation is essential , but further investigations such as immunohistochemistry and fluorescence in situ hybridization are necessary . It is also important to employ flow cytometry as a method of investigation running synchronously or preceding the histopathological approach . Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma . This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma , employing advances in histopathology digitization and automated learning . We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response . Particularly , we propose a strategy that exploits the unpaired image style transfer capabilities of CycleGAN in semi-supervised segmentation . Unlike recent works using adversarial learning for semi-supervised segmentation , we enforce cycle consistency to learn a bidirectional mapping between unpaired images and segmentation masks . This adds an unsupervised regularization effect that boosts the segmentation performance when annotated data is limited . The dual discriminator network includes a fully convolutional discriminator and a typical GAN discriminator , so that the input image can be discriminated on both pixel level and image level . For semi-supervised semantic segmentation , the predicted segmentation results of unlabeled images are selected by image-level discriminator , and then their trustworthy regions are generated by pixel-level discriminator to provide additional supervisory signals . Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our approach outperforms existing semi-supervised semantic image segmentation methods on accuracy . This additional task is only performed during training to improve the semantic segmentation model ’s robustness at test time under several input perturbations . Moreover , we even find that our joint training approach also improves the performance of the model on the original ( supervised ) semantic segmentation task . Our evaluation exhibits a particular novelty in that it allows to mutually compare the effect of input noises and adversarial attacks on the robustness of the semantic segmentation . Secondly , we study semi-supervised segmentation by only providing partial image features . The K-mean clustering is used to convert the semi-supervised segmentation problem into the self-supervised process by identifying the reliable training samples . Thirdly , an unsupervised segmentation algorithm is also developed where image features are completely unknown and can be trained online during segmentation . Markov random field ( MRF ) is extensively used in model-based segmentation of textured images . In this paper , we propose a coupled MRF model and adopt the MAP-MRF framework to solve the semi-supervised segmentation problem . The observed image and the desired labeling are characterized by the conditional Markov ( CM ) model and the multi-level logistic ( MLL ) model , respectively . This method relies on benefiting unsupervised learning , in the form of image reconstruction , for the network training . To this end , it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single task and proposes to learn this united task by a single generative model . This embedding generates a multi-channel output image by superimposing an original input image on its segmentation map . This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the classical multiphase level-set algorithms . We show that the multiphase level-set loss function enables semi-supervised or even unsupervised semantic segmentation . In addition , our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms . For the semantic segmentation task , a classic example for an expensive data labeling process , we do so pixel-wise . Our suggested approaches were applied on the MNIST data-set as a proof of concept for a vision classification task and on the ADE20 K data-set in order to tackle the semi-supervised semantic segmentation problem . We then identify the conditions that allow consistency regularization to work even without such low-density regions . This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant , CowMix , leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets . 2 . Since supervised learning-based semantic image segmentation tasks require large amount of training data , which often costs intensive manual labor , we propose a pipeline that helps create image dataset with semantic level annotations using label transfer . 3 . In addition to the fully unsupervised mode , we also test two semi - supervised settings . The first achieves 88.8 % accuracy on STL10 classification , setting a new global state - of - the - art over all existing methods ( whether supervised , semi - supervised or unsupervised ) . The second shows robustness to 90 % reductions in label coverage , of relevance to applications that wish to make use of small amounts of labels . Furthermore , our non - parametric analysis of the optimal loss function shows its lower and upper bounds are cone - shaped with non - vanishing gradient almost everywhere , implying the generator can be continuously updated even if the loss function is over - trained . Finally , we extend the LS - GAN to a Conditional LS - GAN ( CLS - GAN ) for semi - supervised tasks , and demonstrate it reaches competitive performances on both image generation and classification tasks . bibliography : References We evaluated our models on the CIFAR - 10 krizhevsky2009learning dataset . Following previous work , we make the datasets semi - supervised by only using the provided labels for a subset of the examples in the training set ; the rest are treated as unlabeled examples . Model . As a result , the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution . We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks . section : Abstract In this paper we illustrate how to perform both visual object tracking and semi - supervised video object segmentation , in real - time , with a single simple approach . Our method , dubbed SiamMask , improves the offline training procedure of popular fully - convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task . However , they require fine - tuning at test time , which makes them slow . Semi - supervised video object segmentation . Benchmarks for arbitrary object tracking ( e.g. [ reference ][ reference ] ) assume that trackers receive input frames in a sequential fashion . In particular , we learn refined instance classifiers based on proposal clusters according to both instance scores and spatial relations in an online manner . MIL has many applications to computer vision , such as image classification , weakly supervised semantic segmentation , object detection , object tracking , etc . The strategy of treating proposal clusters as bags was partly inspired by , where proposes to train MIL for patches around groundtruth locations and proposes to train MIL for patches around predicted object locations . Due to stronger multi - scale ability , the Res2Net has activation maps that tend to cover the whole object on big objects such as ‘ bulbul ’ , ‘ mountain dog ’ , ‘ ballpoint ’ , and ‘ mosque ’ , while activation maps of ResNet only cover parts of objects . Such ability of precisely localizing CAM region makes the Res2Net be potentially valuable for object region mining in weakly supervised semantic segmentation tasks . subsection : Object Detection Each batch contains 10 sampled patches of size , of which patches are from source domain , and the other patches from target domain . The cross - entropy loss is used for supervising semantic segmentation in the source domain as in . subsection : Experimental Results
For unsupervised domain adaptation problems , the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification , but usually fails in semantic segmentation tasks in which the latent representations are overcomplex . In this work , we equip the adversarial network with a " significance-aware information bottleneck ( SIB ) " , to address the above problem .
Targeted on maize , we proposed to jointly segment crop and maize tassel . The task is consequently formulated as a semantic segmentation problem . We proposed a region-based approach that leverages the efficient graph-based segmentation algorithm and simple linear iterative clustering ( SLIC ) to generate region proposals .
However , vast majority of the existing methods detect text within local regions , typically through extracting character , word or line level candidates followed by candidate aggregation and false positive elimination , which potentially exclude the effect of wide-scope and long-range contextual cues in the scene . To take full advantage of the rich information available in the whole natural image , we propose to localize text in a holistic manner , by casting scene text detection as a semantic segmentation problem . The proposed algorithm directly runs on full images and produces global , pixel-wise prediction maps , in which detections are subsequently formed .
The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene , targeting different clinical needs . Together with the dataset and taking advantage of advances in semantic segmentation literature , we provide new baselines by training standard fully convolutional networks ( FCNs ) . We perform a comparative study to show that FCNs significantly outperform , without any further postprocessing , prior results in endoluminal scene segmentation , especially with respect to polyp segmentation and localization .
The goal of this work is to densely predict a comparatively large set of affordances given only single RGB images . We approach this task by using a convolutional neural network based on the well-known ResNet architecture , which we blend with refinement modules recently proposed in the semantic segmentation literature . A novel cost function , capable of handling incomplete data , is introduced , which is necessary because we make use of segmentations of objects and their parts to generate affordance maps .
Several traditional and deep learning related techniques providing semantic segmentation for an input from camera is already available , however with the advancement in the computing processor , the progression is on developing the deep learning application replacing traditional methods . This paper presents an approach to combine the input of camera and lidar for semantic segmentation purpose . The proposed model for outdoor scene segmentation is based on the frustum pointnet , and ResNet which utilizes the 3d point cloud and camera input for the 3d bounding box prediction across the moving and non-moving object and thus finally recognizing and understanding the scenario at the point-cloud or pixel level .
We also discuss the common challenges of the current research , and present several valuable growing research points in this field . This survey is expected to familiarize readers with the progress and challenges of semantic segmentation research in the deep learning era .
To achieve this goal , our system connects two separate subsystems together , namely semantic segmentation and portrait synthesis . In the semantic segmentation phase , photographs are segmented into background , hair and skin regions using multiple segmentations method . In the portrait synthesis phase , we treat different regions with different strategies .
However , the use of CRF may cause fragmentation effect around object boundaries . In this paper , we propose the use of a so-called iterative contraction and merging ( ICM ) process to facilitate the semantic segmentation process . Guided by the high-level information from CNN , the ICM process is used as a tool to grow image segments in a bottom-up way and to produce more accurate outcomes in an iterative way .
Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D . In the semantic segmentation step , the labels on the mesh are updated with MRF inference , such that they are compatible with the semantic segmentations in the input images . Also , this step includes prior assumptions about the surface shape of different semantic classes .
Extracting essential contextual information of objects requires CNN models to process features at various scales for effective semantic segmentation . Long et al . proposes one of the earliest methods that enables multi - scale representations of the fully convolutional network ( FCN ) for semantic segmentation task . In DeepLab , Chen
To use the ResNet - 101 model pre - trained on JFT - 300 M data , we split the model into two parts : the first part starts from conv1 block and ends at conv4 block , it is used for feature extraction and is shared by both RPN and box classifier ; the second part consists of the conv5 block , it is used by box classifier . Semantic Segmentation . We use the DeepLab framework with ResNet - 101 base architecture for the task of semantic segmentation .
section : Abstract Semantic image segmentation is an essential component of modern autonomous driving systems , as an accurate understanding of the surrounding scene is crucial to navigation and action planning . Current state - of - the - art approaches in semantic image segmentation rely on pretrained networks that were initially developed for classifying images as a whole .
This is different from our work in at least two ways . Firstly , we treat our reconstruction as a semantic segmentation problem by regressing a volume which is spatially aligned with the image . Secondly , we work from only one image in one single step , regressing a much larger volume of as opposed to the used in .
Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection . subsection : Volumetric Regression Networks
In this section we review approaches that make use of deep learning and CNNs for low - level computer vision tasks , with a focus on semantic image segmentation . A wide variety of approaches have been proposed to tackle the semantic image segmentation task using deep learning . These approaches can be categorized into two main strategies .
document : SegNet : A Deep Convolutional Encoder - Decoder Architecture for Image Segmentation We present a novel and practical deep fully convolutional neural network architecture for semantic pixel - wise segmentation termed SegNet . This core trainable segmentation engine consists of an encoder network , a corresponding decoder network followed by a pixel - wise classification layer .
We also provide a Caffe implementation of SegNet and a web demo at . Deep Convolutional Neural Networks , Semantic Pixel - Wise Segmentation , Indoor Scenes , Road Scenes , Encoder , Decoder , Pooling , Upsampling . section : Introduction
section : Literature Review Semantic pixel - wise segmentation is an active topic of research , fuelled by challenging datasets . Before the arrival of deep networks , the best performing methods mostly relied on hand engineered features classifying pixels independently .
We add a layer skip from input to the reconstruction net . Adding layer skips is successfully used for a semantic segmentation network and we employ a similar idea . Now input image is directly fed into the reconstruction net whenever it is used during recursions .
paragraph : Experiment setup . Since our ultimate goal is to solve the semantic segmentation problem for real images of urban scenes , we take Cityscapes as the target domain and SYNTHIA as the source domain . The Cityscapes validation set is used as our test set .
[ reference ] . The instance embedding provides important details that resolve both inter - and intra - class instance overlap which are not emphasized in the semantic segmentation loss . section : Conclusion and Future Work
These include keypoint matching between 2D images and their corresponding 3D CAD models , or aligning 3D - reconstructed models with ground - truth models to recover the object poses . Gupta propose to predict a semantic segmentation map as well as object pose hypotheses using a CNN and then align the hypotheses with known object CAD models using ICP . These types of methods rely on strong category shape priors or ground - truth object CAD models , which makes them difficult to scale to larger datasets .
Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard in the case of many pathologies , including cancers . However , such approaches are laborious , time consuming and contain a risk for bias or human error .
Nuclei segmentation is a fundamental task in histopathological image analysis . Typically , such segmentation tasks require significant effort to manually generate pixel-wise annotations for fully supervised training .
To date , the successes of neural networks in image segmentation in a number of tasks are comparable to human results and can even exceed them . The paper presents a fast algorithm of histological image segmentation based on the convolutional neural network U-Net . Using this approach allows to get better results in the tasks of medical image segmentation .
Existing state-of-the-art approaches demand extensive amount of supervised training data from pathologists and may still perform poorly in images from unseen tissue types . We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches , of every tissue type . Although our synthetic patches are not always of high quality , we harness the motley crew of generated samples through a generally applicable importance sampling method .
Histopathological image segmentation is a challenging and important topic in medical imaging with tremendous potential impact in clinical practice . State of the art methods relying on hand-crafted annotations that reduce the scope of the solutions since digital histology suffers from standardization and samples differ significantly between cancer phenotypes .
In this paper we present a multiphase level set model for histology image segmentation . Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods .
This paper briefly describes a few methods for segmentation of such images like images of tissues . We analyze some traditional segmentation techniques for histology image segmentation and relate them with the recent framework that performs well on histology dataset . We describe the methods which are helpful for diagnosing cancer and tumors by looking for malignant tissues .
The method is evaluated using two different datasets . Results indicate that accurate histologic segmentation is achievable by this approach .
The labelling of e.g. 20 images per slide is surely less time consuming than the validation of a complete whole slide image processed with a fully automatic , but less reliable , segmentation approach . Finally , it should be highlighted that the proposed interaction protocol could easily be adapted to other histopathological classification or segmentation tasks , also for implementation in a clinical system .
Additionally , some parameter adaptation techniques are also incorporated in the proposed methodology . The modified FPA has been successfully applied for histopathological image segmentation problem . The experimental and computational effort results indicate its effectiveness over existing swarm intelligence algorithms and machine learning methods .
Complex laboratory investigation is necessary for the diagnosis and relevant classification of lymphomas . The classical histopathological morphology and cytology investigation is essential , but further investigations such as immunohistochemistry and fluorescence in situ hybridization are necessary . It is also important to employ flow cytometry as a method of investigation running synchronously or preceding the histopathological approach .
Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma . This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma , employing advances in histopathology digitization and automated learning . We selected 40 digitized whole slide images representing the heterogeneity of osteosarcoma and chemotherapy response .
Particularly , we propose a strategy that exploits the unpaired image style transfer capabilities of CycleGAN in semi-supervised segmentation . Unlike recent works using adversarial learning for semi-supervised segmentation , we enforce cycle consistency to learn a bidirectional mapping between unpaired images and segmentation masks . This adds an unsupervised regularization effect that boosts the segmentation performance when annotated data is limited .
The dual discriminator network includes a fully convolutional discriminator and a typical GAN discriminator , so that the input image can be discriminated on both pixel level and image level . For semi-supervised semantic segmentation , the predicted segmentation results of unlabeled images are selected by image-level discriminator , and then their trustworthy regions are generated by pixel-level discriminator to provide additional supervisory signals . Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our approach outperforms existing semi-supervised semantic image segmentation methods on accuracy .
This additional task is only performed during training to improve the semantic segmentation model ’s robustness at test time under several input perturbations . Moreover , we even find that our joint training approach also improves the performance of the model on the original ( supervised ) semantic segmentation task . Our evaluation exhibits a particular novelty in that it allows to mutually compare the effect of input noises and adversarial attacks on the robustness of the semantic segmentation .
Secondly , we study semi-supervised segmentation by only providing partial image features . The K-mean clustering is used to convert the semi-supervised segmentation problem into the self-supervised process by identifying the reliable training samples . Thirdly , an unsupervised segmentation algorithm is also developed where image features are completely unknown and can be trained online during segmentation .
Markov random field ( MRF ) is extensively used in model-based segmentation of textured images . In this paper , we propose a coupled MRF model and adopt the MAP-MRF framework to solve the semi-supervised segmentation problem . The observed image and the desired labeling are characterized by the conditional Markov ( CM ) model and the multi-level logistic ( MLL ) model , respectively .
This method relies on benefiting unsupervised learning , in the form of image reconstruction , for the network training . To this end , it puts forward an idea of defining a new embedding that allows uniting the main supervised task of semantic segmentation and an auxiliary unsupervised task of image reconstruction into a single task and proposes to learn this united task by a single generative model . This embedding generates a multi-channel output image by superimposing an original input image on its segmentation map .
This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the classical multiphase level-set algorithms . We show that the multiphase level-set loss function enables semi-supervised or even unsupervised semantic segmentation . In addition , our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms .
For the semantic segmentation task , a classic example for an expensive data labeling process , we do so pixel-wise . Our suggested approaches were applied on the MNIST data-set as a proof of concept for a vision classification task and on the ADE20 K data-set in order to tackle the semi-supervised semantic segmentation problem .
We then identify the conditions that allow consistency regularization to work even without such low-density regions . This allows us to generalize the recently proposed CutMix augmentation technique to a powerful masked variant , CowMix , leading to a successful application of consistency regularization in the semi-supervised semantic segmentation setting and reaching state-of-the-art results in several standard datasets .
2 . Since supervised learning-based semantic image segmentation tasks require large amount of training data , which often costs intensive manual labor , we propose a pipeline that helps create image dataset with semantic level annotations using label transfer . 3 .
In addition to the fully unsupervised mode , we also test two semi - supervised settings . The first achieves 88.8 % accuracy on STL10 classification , setting a new global state - of - the - art over all existing methods ( whether supervised , semi - supervised or unsupervised ) . The second shows robustness to 90 % reductions in label coverage , of relevance to applications that wish to make use of small amounts of labels .
Furthermore , our non - parametric analysis of the optimal loss function shows its lower and upper bounds are cone - shaped with non - vanishing gradient almost everywhere , implying the generator can be continuously updated even if the loss function is over - trained . Finally , we extend the LS - GAN to a Conditional LS - GAN ( CLS - GAN ) for semi - supervised tasks , and demonstrate it reaches competitive performances on both image generation and classification tasks . bibliography : References
We evaluated our models on the CIFAR - 10 krizhevsky2009learning dataset . Following previous work , we make the datasets semi - supervised by only using the provided labels for a subset of the examples in the training set ; the rest are treated as unlabeled examples . Model .
As a result , the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution . We show how the adversarial autoencoder can be used in applications such as semi - supervised classification , disentangling style and content of images , unsupervised clustering , dimensionality reduction and data visualization . We performed experiments on MNIST , Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi - supervised classification tasks .
section : Abstract In this paper we illustrate how to perform both visual object tracking and semi - supervised video object segmentation , in real - time , with a single simple approach . Our method , dubbed SiamMask , improves the offline training procedure of popular fully - convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task .
However , they require fine - tuning at test time , which makes them slow . Semi - supervised video object segmentation . Benchmarks for arbitrary object tracking ( e.g. [ reference ][ reference ] ) assume that trackers receive input frames in a sequential fashion .
In particular , we learn refined instance classifiers based on proposal clusters according to both instance scores and spatial relations in an online manner . MIL has many applications to computer vision , such as image classification , weakly supervised semantic segmentation , object detection , object tracking , etc . The strategy of treating proposal clusters as bags was partly inspired by , where proposes to train MIL for patches around groundtruth locations and proposes to train MIL for patches around predicted object locations .
Due to stronger multi - scale ability , the Res2Net has activation maps that tend to cover the whole object on big objects such as ‘ bulbul ’ , ‘ mountain dog ’ , ‘ ballpoint ’ , and ‘ mosque ’ , while activation maps of ResNet only cover parts of objects . Such ability of precisely localizing CAM region makes the Res2Net be potentially valuable for object region mining in weakly supervised semantic segmentation tasks . subsection : Object Detection
Each batch contains 10 sampled patches of size , of which patches are from source domain , and the other patches from target domain . The cross - entropy loss is used for supervising semantic segmentation in the source domain as in . subsection : Experimental Results
Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation Joint crop and tassel segmentation in the wild Scene Text Detection via Holistic, Multi-Channel Prediction A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Learning to Segment Affordances Real-Time 3-D Segmentation on An Autonomous Embedded System: using Point Cloud and Camera A Brief Survey on Semantic Segmentation with Deep Learning Semantic Segmentation Based Automatic Two-Tone Portrait Synthesis Semantic segmentation based on iterative contraction and merging Semantically Informed Multiview Surface Refinement Res2Net: A New Multi-scale Backbone Architecture Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Conditional Random Fields as Recurrent Neural Networks SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Deeply-Recursive Convolutional Network for Image Super-Resolution Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Recurrent Pixel Embedding for Instance Grouping PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation Investigating and Assessing the Dermoepidermal Junction with Multiphoton Microscopy and Deep Learning Weakly Supervised Deep Nuclei Segmentation using Points Annotation in Histopathology Images Using Convolutional Neural Networks in the Problem of Cell Nuclei Segmentation on Histological Images Robust Histopathology Image Analysis: To Label or to Synthesize? Weakly supervised multiple instance learning histopathological tumor segmentation Multiphase Level Set Model with Local K-means Energy for Histology Image Segmentation Detection of malignant tissues: Analysis on segmentation of histology images A new segmentation framework for infrared spectroscopic imaging using frequent pattern mining SEMI-AUTOMATIC CLASSIFICATION OF HISTOPATHOLOGICAL IMAGES: DEALING WITH INTER-SLIDE VARIATIONS Toward the modification of flower pollination algorithm in clustering-based image segmentation [A complex diagnostic approach in lymphomas: practical aspect in short case reports]. Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models Revisiting CycleGAN for semi-supervised segmentation Semi-supervised semantic image segmentation using dual discriminator adversarial networks Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation A study of supervised, semi-supervised and unsupervised multiscale Bayesian image segmentation Semi-Supervised Segmentation of Textured Images by Using Coupled MRF Model Image Embedded Segmentation: Combining Supervised and Unsupervised Objectives through Generative Adversarial Networks Multiphase Level-Set Loss for Semi-Supervised and Unsupervised Segmentation with Deep Learning Improved Training for Self-Training Semi-supervised semantic segmentation needs strong, high-dimensional perturbations Semantic Information Extraction from UAV Imagery and Aerial Imagery Invariant Information Clustering for Unsupervised Image Classification and Segmentation Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Semi-Supervised Sequence Modeling with Cross-View Training Adversarial Autoencoders Fast Online Object Tracking and Segmentation: A Unifying Approach PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Res2Net: A New Multi-scale Backbone Architecture ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes
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2019 2015 2016 2016 2017 2019 2020 2017 2017 2017 2019 2017 2017 2017 2015 2017 2016 2017 2018 2018 2019 2019 2019 2019 2020 2011 2015 2011 2016 2020 2014 2019 2019 2019 2020 2002 2005 2020 2019 2017 2019 2019 2019 2019 2018 2015 2019 2020 2019 2018
ICCV Workshops ICIG (3) ICIP IEEE transactions on pattern analysis and machine intelligence 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2015 IEEE International Conference on Computer Vision (ICCV) IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition MIDL CVPR HISB ISBI 2019 IEEE/CVF International Conference on Computer Vision (ICCV) International Journal of Computer Vision EMNLP ArXiv 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE transactions on pattern analysis and machine intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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