There is a close relationship between SVMs and the RadialBasis Function ( RBF ) classifiers . Both have found numerous applications such as in optical character recognition , object detection , face verification , text categorization , and so on . The result demonstrated that the accura cy and generalization performance of SVMGaussian Radial Basis kernel functionis better than RBFN . Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection , but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation . In this work we evaluate the performance of Random Forest on the ImageNet dataset , and compare it to standard approaches in the state-of-the-art . A CNN is used to localize multiple human beings from frame to frame in a video stream . This deep CNN is known as Faster R-CNN which achieved the state of the art performance in object detection problem . In the tracking process , we solve the data association problem in visual tracking using HA . It also opens the possibility of using the GHT for detecting objects subject to more complex transformations such as affine or perspective . The relationship also suggests the possibility of using alternative voting functions which may speed up the object detection process . The task of 6d pose estimation is handled in the Cartesian space by finding an interest window around the segmented object and 3d geometry operations . The interest window is later used for feature extraction in the subsequent camera frames to speed up the object detection process . This also allows for an efficient pose tracking of scenes where there are significantly large false matches between feature correspondences due to scene clutter . According to the detecting process , traversing is carried out on an input image to be detected according to the preset windows ; during traversing , color features , gradient features and position dimension features of the image to be detected are extracted , wherein the extracted position dimension features comprise the probability of target appearing at the different positions in the image to be detected is determined according to the pre-acquired position probability table ; the extracted gradient features are input to a first support vector machine ( SVM ) to be trained , and then the output of the first SVM is obtained ; the extracted color features , the output of the first SVM and the determined probability are input to a second SVM classifier obtained through pre-training , and the target detecting result is output . Object detecting is carried out through the three features and the cascaded SVM classifier , and therefore not only is a specific object detected , but also all areas , namely general objects possibly with the available information in the image to be detected can be detected . subsection : Multi - scale Context - aware Network Visual context is an important component to assist visual - related tasks , such as object recognition and object detection . Typical convolutional neural networks model context information through hierarchical convolution and pooling . We conjecture that more training data will lead to a larger gap , as observed on the ImageNet - 5 K set . It is also worth noting that recently ResNeXt has been adopted in Mask R - CNN [ reference ] that achieves state - of - the - art results on COCO instance segmentation and object detection tasks . section : A. Implementation Details : CIFAR section : Abstract This paper proposes a Fast Region - based Convolutional Network method ( Fast R - CNN ) for object detection . Fast R - CNN builds on previous work to efficiently classify object proposals using deep convolutional networks . Despite the recent success of video object detection on Desktop GPUs , its architecture is still far too heavy for mobiles . It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources . Abstract It is challenging for video salient object detection in the pursuit of high accuracy and fast speed with large amount of calculation in spatiotemporal domain . Most of existing methods use complex models with massive number of parameters to detect salient regions in video and cost a lot of time . We further introduce a feature enhancement module based on the ConvGRU structure , which helps to deal with deterioration of image quality in video object detection and re-identification , such as motion blur and camera losing focus . Experimental results show that the proposed method achieves competitive performance compared with state-of-the-art methods in video-based object detection , cross-dataset person re-identification , and multi-object tracking . Finally , in order to obtain a high-quality saliency map with well-defined boundaries , the output of aggregation network and the current frame are used to perform spatial refinement , yielding the final saliency map for the current frame . The extensive qualitative and quantitative experiments on two challenging video datasets show that the proposed model consistently outperforms the state-of-the-art saliency models for detecting salient objects in videos . In this article , we focus on exploring the relationship between them . Firstly , we propose a network called fixation assisted video salient object detection network ( FAVSODNet ) , which uses the eye gaze information in videos to assist in detecting video salient objects . A fixation assisted module ( FAM ) is designed to connect FP task and SOD task deeply . From the user data , we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video , and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects . To the best of our knowledge , it is currently the largest dataset for video-based salient object detection . Based on this dataset , this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders . In the video salient object detection domain , the optical flow technique is widely used to extract temporal saliency . The big issue with this approach is that the global motion , when it exists , may be wrongly detected as a salient object . Using the same epidemiological techniques that had stood him in good stead during the investigation of lymphoma , he verified the link between several diseases of the Western world and lack of fibre in the diet , earning for himself the nickname " the bran man . " Brian Kellock documents these feats of medical detection with great sympathy . He traces the virtues of this perspicacious Irishman from whom we can all learn : ( 1 ) An unabounded enthusiasm and native frugality which made important medical advances on a shoe string budget . Based on the medical image detection after segmentation , the features from each domain such as spatial , texture , spectral domain have been extracted for detection . With no regard to other unknown features , these features are sometime redundant . We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets , including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset . Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure . The research in Computer vision has or similar pointed out that the object recognition can be regarded as cooperation activity of data-driven processes with expectation-driven processes . The paper suggests a knowledge structure suitable for driving medical object recognition . The knowledge is organized in a network in which each node is a potential sub-model useful in the instantiation process . A new method for the extraction of features from stationary stochastic processes has been applied to a medical detection problem . It is an illustration of new possibilities with automatic time series modeling . The intention of this article is to cover those approaches introduced and to make a map for ANN techniques used for medical image processing . Instead of trying to cover all the issues and research aspects of ANNs in medical image processing , we focus our discussion on three major topics : medical image preprocessing ( i.e. de-noising and enhancement ) , medical image segmentation , and medical image object detection and recognition . This paper aims to present a review of artificial neural networks ( ANN ) used in medical image processing . Instead of trying to cover all the issues and research aspects of ANNs in medical image processing , we focus our discussion on three major topics : medical image preprocessing ( i.e.de-noising and enhancement ) , medical image segmentation , and medical image object detection and recognition . Main advantages and drawbacks of the methods are mentioned . It integrated the circadian detecting technology , Bluetooth technology , GPRS technology , artificial intelligence medical diagnosis technology and alarm technology . It is an uninterrupted real-time working system which includes the subsystem of medical detecting , long distance transmission , date storage and analysis , alarm etc . It is mainly used to detect the blood pressure and pulse of the cardiovascular and cerebrovascular disease patient , send the monitor data to the information analyzing center by the aid of Bluetooth and mobile communication device . Overall , this technology would allow for a decreased sensor thickness , accompanied with a high collector efficiency , and consequently improved signal characteristics . Therefore , the signal quality of an imaging sensor as applied to medical detector technology , radio astronomy , aviation security , surveillance and nondestructive inspection , and other industrial areas will be significantly improved . Image segmentation plays a vital role in medical image analysis and detection . In this paper , automatic brain tumor detection from magnetic resonance image using wavelet based genetic algorithmic approach was implemented . The next significant problem considered in the work is 2D object detection . Although medical object detection is not integrated into the developed scanning systems , it can be used as a possible further extension of the systems for automatic detection of specific anatomical structures . We analyse the existent object detection methods and introduce a modification of the one based on the popular Generalized Hough Transform ( GHT ) . Human pose estimation and semantic part segmentation are two complementary tasks in computer vision . In this paper , we propose to solve the two tasks jointly for natural multi-person images , in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations . Since it has been shown that the kind of text content is very harmful to young children , several measures have been taken to detect the objectionable text content . Unlike current methods , a scene-based method is proposed to recognize the objectionable text with aim at improving the performance , especially in the semantic detection . A scene which is defined by a set of sentences is assigned as the topics of objectionable content . Object detection and semantic part detection are two tasks that can mutually benefit each other . Thus , in this paper we propose an approach to perform joint detection . This is very important in computer vision , as it provides the possibility to parse an object as human do , and helps us better understand object detection algorithms . Also , detecting semantic parts is very challenging especially when the parts are partially or fully occluded . In this scenario , the popular proposal-based methods like Faster-RCNN often produce unsatisfactory results , because both the proposal extraction and classification stages may be confused by the irrelevant occluders . However , indefinitely acquiring large amounts of annotations is not a sustainable process , and one can wonder if there exists a volume of annotations beyond which a task can be considered as solved or at least saturated . In this work , we study this crucial question for the task of detecting semantic parts which are often seen as a natural way to share knowledge between categories . To this end , on a large dataset of 15,000 images from 100 different animal classes annotated with semantic parts , we consider the two following research questions : ( i ) are semantic parts really visually shareable between classes ? In experiments , we show three aspects : 1 ) the AoT is more efficient than the frequently used octree method in space representation ; 2 ) the learned 3D car template matches the state-of-the art performances on car detection and pose estimation in a public multi-view car dataset ; and 3 ) in our new dataset , the learned 3D template solves the joint task of simultaneous object detection , pose/view estimation , and part localization . It can generalize over unseen views and performs better than the version 5 of the DPM model in terms of object detection and semantic part localization . Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance , aiming at recognizing hundreds of sub-categories belonging to the same basic-level category . Since two different sub-categories is distinguished only by the subtle differences in some specific parts , semantic part localization is crucial for fine-grained image classification . Most previous works improve the accuracy by looking for the semantic parts , but rely heavily upon the use of the object or part annotations of images whose labeling are costly . Mimimorphic malware uses instruction-syntax-aware high-order mimic functions to transform its binary into mimicry executables that exhibit high similarity to benign programs in terms of statistical properties and semantic characteristics . We implement a prototype of the mimimorphic engine on the Intel x86 platform , and evaluate its capability of evading statistical anomaly detection and semantic analysis detection techniques . Our experimental results demonstrate that the mimicry executables are indistinguishable from benign programs in terms of byte frequency distribution and entropy , as well as control flow fingerprint . The research draws on the possibility to enrich the H-BIM model with a semantic layer that takes into account the vulnerability indicators as defined in 2011 Italian Guidelines for the assessment and reduction of seismic risk of cultural heritage . Starting from 3D data survey , passing through the historical and constructive analysis , and the semantic classification of parts , the workflow foresaw the setting up of a semantic layer creating parameters to be assigned to each architectural component of the building and according an expeditious check-list which focuses on empirical knowledge of bell towers . This kind of information stored in the model could be very useful in situations that require particularly timely decision-making , such as in the case of calamitous events . Recent work has demonstrated the emergence of semantic object-part detectors in activation patterns of convolutional neural networks ( CNNs ) , but did not account for the distributed multi-layer neural activations in such networks . In this work , we propose a novel method to extract distributed patterns of activations from a CNN and show that such patterns correspond to high-level visual attributes . We enrich knowledge about mobile ' application behavior ' and expose this knowledge through the Mobipedia knowledge-base . We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth , low energy beacons and Nearby Messaging services from Google . In this paper , we propose an adversarial learning method for domain adaptation in the context of semantic segmentation . Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains , we adopt adversarial learning in the output space . To further enhance the adapted model , we construct a multi - level adversarial network to effectively perform output space domain adaptation at different feature levels . The addition of a deconvolution layer is a popular choice for recovering resolution from max - pooling and other image down - sampling layers . This approach has been successfully used in visualizing layer activations and for generating semantic segmentations using high level features from the network . It is trivial to show that the bicubic interpolation used in SRCNN is a special case of the deconvolution layer , as suggested already in . Most segmentation error occurs along part boundaries . There are also cases where the semantic definition of parts has inherent ambiguities . We also observe a third type of error pattern , in which our prediction might miss a certain part completely , as is shown in the fourth block . Such a combined representation comprehensively maintains the contextual information , which is crucial for generating smooth and accurate parsing results . Formally , let the instance - agnostic parsing map be denoted by , then Similar to the first sub - net , we propose to learn by minimizing : where is the g lobal p arsing loss for semantic part labelling . is a standard pixel - wise cross - entropy loss calculated based on the multi - class pixel - wise annotations to learn . document : Part - based R - CNNs for Fine - grained Category Detection Semantic part localization can facilitate fine - grained categorization by explicitly isolating subtle appearance differences associated with specific object parts . Methods for pose - normalized representations have been proposed , but generally presume bounding box annotations at test time due to the difficulty of object detection .
There is a close relationship between SVMs and the RadialBasis Function ( RBF ) classifiers . Both have found numerous applications such as in optical character recognition , object detection , face verification , text categorization , and so on . The result demonstrated that the accura cy and generalization performance of SVMGaussian Radial Basis kernel functionis better than RBFN .
Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection , but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation . In this work we evaluate the performance of Random Forest on the ImageNet dataset , and compare it to standard approaches in the state-of-the-art .
A CNN is used to localize multiple human beings from frame to frame in a video stream . This deep CNN is known as Faster R-CNN which achieved the state of the art performance in object detection problem . In the tracking process , we solve the data association problem in visual tracking using HA .
It also opens the possibility of using the GHT for detecting objects subject to more complex transformations such as affine or perspective . The relationship also suggests the possibility of using alternative voting functions which may speed up the object detection process .
The task of 6d pose estimation is handled in the Cartesian space by finding an interest window around the segmented object and 3d geometry operations . The interest window is later used for feature extraction in the subsequent camera frames to speed up the object detection process . This also allows for an efficient pose tracking of scenes where there are significantly large false matches between feature correspondences due to scene clutter .
According to the detecting process , traversing is carried out on an input image to be detected according to the preset windows ; during traversing , color features , gradient features and position dimension features of the image to be detected are extracted , wherein the extracted position dimension features comprise the probability of target appearing at the different positions in the image to be detected is determined according to the pre-acquired position probability table ; the extracted gradient features are input to a first support vector machine ( SVM ) to be trained , and then the output of the first SVM is obtained ; the extracted color features , the output of the first SVM and the determined probability are input to a second SVM classifier obtained through pre-training , and the target detecting result is output . Object detecting is carried out through the three features and the cascaded SVM classifier , and therefore not only is a specific object detected , but also all areas , namely general objects possibly with the available information in the image to be detected can be detected .
subsection : Multi - scale Context - aware Network Visual context is an important component to assist visual - related tasks , such as object recognition and object detection . Typical convolutional neural networks model context information through hierarchical convolution and pooling .
We conjecture that more training data will lead to a larger gap , as observed on the ImageNet - 5 K set . It is also worth noting that recently ResNeXt has been adopted in Mask R - CNN [ reference ] that achieves state - of - the - art results on COCO instance segmentation and object detection tasks . section : A. Implementation Details : CIFAR
section : Abstract This paper proposes a Fast Region - based Convolutional Network method ( Fast R - CNN ) for object detection . Fast R - CNN builds on previous work to efficiently classify object proposals using deep convolutional networks .
Despite the recent success of video object detection on Desktop GPUs , its architecture is still far too heavy for mobiles . It is also unclear whether the key principles of sparse feature propagation and multi-frame feature aggregation apply at very limited computational resources .
Abstract It is challenging for video salient object detection in the pursuit of high accuracy and fast speed with large amount of calculation in spatiotemporal domain . Most of existing methods use complex models with massive number of parameters to detect salient regions in video and cost a lot of time .
We further introduce a feature enhancement module based on the ConvGRU structure , which helps to deal with deterioration of image quality in video object detection and re-identification , such as motion blur and camera losing focus . Experimental results show that the proposed method achieves competitive performance compared with state-of-the-art methods in video-based object detection , cross-dataset person re-identification , and multi-object tracking .
Finally , in order to obtain a high-quality saliency map with well-defined boundaries , the output of aggregation network and the current frame are used to perform spatial refinement , yielding the final saliency map for the current frame . The extensive qualitative and quantitative experiments on two challenging video datasets show that the proposed model consistently outperforms the state-of-the-art saliency models for detecting salient objects in videos .
In this article , we focus on exploring the relationship between them . Firstly , we propose a network called fixation assisted video salient object detection network ( FAVSODNet ) , which uses the eye gaze information in videos to assist in detecting video salient objects . A fixation assisted module ( FAM ) is designed to connect FP task and SOD task deeply .
From the user data , we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video , and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects . To the best of our knowledge , it is currently the largest dataset for video-based salient object detection . Based on this dataset , this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders .
In the video salient object detection domain , the optical flow technique is widely used to extract temporal saliency . The big issue with this approach is that the global motion , when it exists , may be wrongly detected as a salient object .
Using the same epidemiological techniques that had stood him in good stead during the investigation of lymphoma , he verified the link between several diseases of the Western world and lack of fibre in the diet , earning for himself the nickname " the bran man . " Brian Kellock documents these feats of medical detection with great sympathy . He traces the virtues of this perspicacious Irishman from whom we can all learn : ( 1 ) An unabounded enthusiasm and native frugality which made important medical advances on a shoe string budget .
Based on the medical image detection after segmentation , the features from each domain such as spatial , texture , spectral domain have been extracted for detection . With no regard to other unknown features , these features are sometime redundant .
We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets , including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset . Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure .
The research in Computer vision has or similar pointed out that the object recognition can be regarded as cooperation activity of data-driven processes with expectation-driven processes . The paper suggests a knowledge structure suitable for driving medical object recognition . The knowledge is organized in a network in which each node is a potential sub-model useful in the instantiation process .
A new method for the extraction of features from stationary stochastic processes has been applied to a medical detection problem . It is an illustration of new possibilities with automatic time series modeling .
The intention of this article is to cover those approaches introduced and to make a map for ANN techniques used for medical image processing . Instead of trying to cover all the issues and research aspects of ANNs in medical image processing , we focus our discussion on three major topics : medical image preprocessing ( i.e. de-noising and enhancement ) , medical image segmentation , and medical image object detection and recognition .
This paper aims to present a review of artificial neural networks ( ANN ) used in medical image processing . Instead of trying to cover all the issues and research aspects of ANNs in medical image processing , we focus our discussion on three major topics : medical image preprocessing ( i.e.de-noising and enhancement ) , medical image segmentation , and medical image object detection and recognition . Main advantages and drawbacks of the methods are mentioned .
It integrated the circadian detecting technology , Bluetooth technology , GPRS technology , artificial intelligence medical diagnosis technology and alarm technology . It is an uninterrupted real-time working system which includes the subsystem of medical detecting , long distance transmission , date storage and analysis , alarm etc . It is mainly used to detect the blood pressure and pulse of the cardiovascular and cerebrovascular disease patient , send the monitor data to the information analyzing center by the aid of Bluetooth and mobile communication device .
Overall , this technology would allow for a decreased sensor thickness , accompanied with a high collector efficiency , and consequently improved signal characteristics . Therefore , the signal quality of an imaging sensor as applied to medical detector technology , radio astronomy , aviation security , surveillance and nondestructive inspection , and other industrial areas will be significantly improved .
Image segmentation plays a vital role in medical image analysis and detection . In this paper , automatic brain tumor detection from magnetic resonance image using wavelet based genetic algorithmic approach was implemented .
The next significant problem considered in the work is 2D object detection . Although medical object detection is not integrated into the developed scanning systems , it can be used as a possible further extension of the systems for automatic detection of specific anatomical structures . We analyse the existent object detection methods and introduce a modification of the one based on the popular Generalized Hough Transform ( GHT ) .
Human pose estimation and semantic part segmentation are two complementary tasks in computer vision . In this paper , we propose to solve the two tasks jointly for natural multi-person images , in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations .
Since it has been shown that the kind of text content is very harmful to young children , several measures have been taken to detect the objectionable text content . Unlike current methods , a scene-based method is proposed to recognize the objectionable text with aim at improving the performance , especially in the semantic detection . A scene which is defined by a set of sentences is assigned as the topics of objectionable content .
Object detection and semantic part detection are two tasks that can mutually benefit each other . Thus , in this paper we propose an approach to perform joint detection .
This is very important in computer vision , as it provides the possibility to parse an object as human do , and helps us better understand object detection algorithms . Also , detecting semantic parts is very challenging especially when the parts are partially or fully occluded . In this scenario , the popular proposal-based methods like Faster-RCNN often produce unsatisfactory results , because both the proposal extraction and classification stages may be confused by the irrelevant occluders .
However , indefinitely acquiring large amounts of annotations is not a sustainable process , and one can wonder if there exists a volume of annotations beyond which a task can be considered as solved or at least saturated . In this work , we study this crucial question for the task of detecting semantic parts which are often seen as a natural way to share knowledge between categories . To this end , on a large dataset of 15,000 images from 100 different animal classes annotated with semantic parts , we consider the two following research questions : ( i ) are semantic parts really visually shareable between classes ?
In experiments , we show three aspects : 1 ) the AoT is more efficient than the frequently used octree method in space representation ; 2 ) the learned 3D car template matches the state-of-the art performances on car detection and pose estimation in a public multi-view car dataset ; and 3 ) in our new dataset , the learned 3D template solves the joint task of simultaneous object detection , pose/view estimation , and part localization . It can generalize over unseen views and performs better than the version 5 of the DPM model in terms of object detection and semantic part localization .
Fine-grained image classification is challenging due to the large intra-class variance and small inter-class variance , aiming at recognizing hundreds of sub-categories belonging to the same basic-level category . Since two different sub-categories is distinguished only by the subtle differences in some specific parts , semantic part localization is crucial for fine-grained image classification . Most previous works improve the accuracy by looking for the semantic parts , but rely heavily upon the use of the object or part annotations of images whose labeling are costly .
Mimimorphic malware uses instruction-syntax-aware high-order mimic functions to transform its binary into mimicry executables that exhibit high similarity to benign programs in terms of statistical properties and semantic characteristics . We implement a prototype of the mimimorphic engine on the Intel x86 platform , and evaluate its capability of evading statistical anomaly detection and semantic analysis detection techniques . Our experimental results demonstrate that the mimicry executables are indistinguishable from benign programs in terms of byte frequency distribution and entropy , as well as control flow fingerprint .
The research draws on the possibility to enrich the H-BIM model with a semantic layer that takes into account the vulnerability indicators as defined in 2011 Italian Guidelines for the assessment and reduction of seismic risk of cultural heritage . Starting from 3D data survey , passing through the historical and constructive analysis , and the semantic classification of parts , the workflow foresaw the setting up of a semantic layer creating parameters to be assigned to each architectural component of the building and according an expeditious check-list which focuses on empirical knowledge of bell towers . This kind of information stored in the model could be very useful in situations that require particularly timely decision-making , such as in the case of calamitous events .
Recent work has demonstrated the emergence of semantic object-part detectors in activation patterns of convolutional neural networks ( CNNs ) , but did not account for the distributed multi-layer neural activations in such networks . In this work , we propose a novel method to extract distributed patterns of activations from a CNN and show that such patterns correspond to high-level visual attributes .
We enrich knowledge about mobile ' application behavior ' and expose this knowledge through the Mobipedia knowledge-base . We also extend context synthesis for semantic presence detection on mobile devices by combining Bluetooth , low energy beacons and Nearby Messaging services from Google .
In this paper , we propose an adversarial learning method for domain adaptation in the context of semantic segmentation . Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains , we adopt adversarial learning in the output space . To further enhance the adapted model , we construct a multi - level adversarial network to effectively perform output space domain adaptation at different feature levels .
The addition of a deconvolution layer is a popular choice for recovering resolution from max - pooling and other image down - sampling layers . This approach has been successfully used in visualizing layer activations and for generating semantic segmentations using high level features from the network . It is trivial to show that the bicubic interpolation used in SRCNN is a special case of the deconvolution layer , as suggested already in .
Most segmentation error occurs along part boundaries . There are also cases where the semantic definition of parts has inherent ambiguities . We also observe a third type of error pattern , in which our prediction might miss a certain part completely , as is shown in the fourth block .
Such a combined representation comprehensively maintains the contextual information , which is crucial for generating smooth and accurate parsing results . Formally , let the instance - agnostic parsing map be denoted by , then Similar to the first sub - net , we propose to learn by minimizing : where is the g lobal p arsing loss for semantic part labelling . is a standard pixel - wise cross - entropy loss calculated based on the multi - class pixel - wise annotations to learn .
document : Part - based R - CNNs for Fine - grained Category Detection Semantic part localization can facilitate fine - grained categorization by explicitly isolating subtle appearance differences associated with specific object parts . Methods for pose - normalized representations have been proposed , but generally presume bounding box annotations at test time due to the difficulty of object detection .
A Software Measurement Using Artificial Neural Network and Support Vector Machine Evaluation of Random Forests on large-scale classification problems using a Bag-of-Visual-Words representation Multiple human tracking in drone image Probabilistic relaxation and the Hough transform Real-time pose estimation of rigid objects using RGB-D imagery General object detecting method and system Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Aggregated Residual Transformations for Deep Neural Networks Fast R-CNN Towards High Performance Video Object Detection for Mobiles High-speed video salient object detection with temporal propagation using correlation filter Multi-object Tracking by Joint Detection and Identification Learning Video Saliency Detection Using Deep Convolutional Neural Networks Eye Fixation Assisted Detection of Video Salient Objects A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for Video-Based Salient Object Detection Fast filtering-based temporal saliency detection using Minimum Barrier Distance Autumn Books: The bran man Features selection analysis for pattern classification CAggNet: Crossing Aggregation Network for Medical Image Segmentation Knowledge Structure In Object Recognition Feature extraction with time series models: application to lung sounds Neural network in medical image processing Current Status and Future Potential of Neural Networks Used for Medical Image Processing Real-time Health Information Acquisition and Alarm System Based on Bluetooth and GPRS Communication Technologies A novel sensor for X-ray imaging applications A Novel MRI Brain Tumor Detection Based on Genetic Algorithm Medical image analysis methods for anatomical surface reconstruction using tracked 3D ultrasound Joint Multi-person Pose Estimation and Semantic Part Segmentation Hierarchical semantic model for objectionable Web text content detection Exploiting LSTM for Joint Object and Semantic Part Detection DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion Generalizing Semantic Part Detectors Across Domains Learning 3D Object Templates by Quantizing Geometry and Appearance Spaces Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification Mimimorphism: a new approach to binary code obfuscation HBIM FOR THE SEISMIC VULNERABILITY ASSESSMENTOF TRADITIONAL BELL TOWERS Explaining Distributed Neural Activations via Unsupervised Learning Context-Dependent Privacy and Security Management on Mobile Devices Learning to Adapt Structured Output Space for Semantic Segmentation Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing Part-Based R-CNNs for Fine-Grained Category Detection
a0ca2d7cde72eea9c38f504b863ffdb7fe6a0dc3 173c6c96a72d883debdb9d0eb031756fb06127d7 632cf8c3023ed55e71c08f00dfb7895e89c37764 271c8f623f230604899e687049a3de31257b4b84 afe03819adab7982ba395649490acc27b9a16683 312919a3387012de7dbf38e7e9359bc1d4f75a70 e4d4346bd415c6fa9187c16a9b7f5c69f48f1ec4 7b73a1f7b749d96305226a64d8644f96b92fe797 9f981810441013520bd4c2e70b455025332ab134 2550dfbbc2d73236aa20a6260c244b826c1a35d6 fe14a04683dd695def544da7b1d3d797041c15f1 55604c73022a40953ed0ff67c8b0e543ae6e9451 972ef8223e22e0ede8f5ae17cf10edd780ff57f6 09752443064819f865f3b094c61abe60431b14ee 3a15cf2594835dd2f0dab4b5cc47ac7eed3319d4 193b7e753b97d7997a198cacd7c35c7c700d79be 40a6402789d2045b515e9c2bae483b5cb5f00155 8919f2b1aad59b3c7a2cd4bcaf54de8a710b119c dd1ef79f194d4ddb232c94b2dfe390b35ea301aa d9754be54eeae6d731ed956ca785391b5c559227 d3319d5d507a34c84ad706de9718790c8119f9c7 b9358caf0a6909872f82bf918361569c1ca160dc 4f8a598d6c681604ca25f571c90d8534d9d12d6d 6c024b17ce6426a0ba85411d6d9ede0d397084f4 fc027fccb19512a439fc17181c34ee1c3aad51b5 a0b46d7ceef8d9f0ef87766bde81a4140190a085 388358ad13be5199af58c7245aaa8f55d5520858 c7780cff11068fecb322a43e459c56267a88aee7 53e69513658c7f20c40e15b1640ba48327efeef6 f3159fbd97282cbb0a139794b5b17341ecfb3ea2 ba1046217f50c02c852c595a4fe81d1c1126d068 71dd100ef9c731c31a566eecd598962f8a8dc24a b3d59d2a9cd269300a835cf31dbc4076a139d944 8134b59fc1770286447744d460cd9462267e15c8 a73e37bda21208fc865803055f47837772a03bbd
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Medicine"] ["Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2013 2014 2019 2000 2013 2014 2017 2017 2015 2018 2019 2019 2018 2019 2018 2017 1985 2007 2020 1988 1999 2012 2011 2006 2000 2011 2014 2017 2012 2018 2017 2017 2015 2017 2010 2019 2017 2017 2018 2016 2017 2018 2014
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE International Conference on Computer Vision (ICCV) PRCV (2) BICS ICME Workshops SMC ACCV (5) Domain Adaptation in Computer Vision Applications AAAI ACM Conference on Computer and Communications Security CVPR Workshops 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ECCV
https://api.semanticscholar.org/a0ca2d7cde72eea9c38f504b863ffdb7fe6a0dc3 https://api.semanticscholar.org/173c6c96a72d883debdb9d0eb031756fb06127d7 https://api.semanticscholar.org/632cf8c3023ed55e71c08f00dfb7895e89c37764 https://api.semanticscholar.org/271c8f623f230604899e687049a3de31257b4b84 https://api.semanticscholar.org/afe03819adab7982ba395649490acc27b9a16683 https://api.semanticscholar.org/312919a3387012de7dbf38e7e9359bc1d4f75a70 https://www.semanticscholar.org/paper/605886b07ec27492e8d5bc48f79ddc39c02ec190 https://www.semanticscholar.org/paper/f6e0856b4a9199fa968ac00da612a9407b5cb85c https://www.semanticscholar.org/paper/7ffdbc358b63378f07311e883dddacc9faeeaf4b https://api.semanticscholar.org/e4d4346bd415c6fa9187c16a9b7f5c69f48f1ec4 https://api.semanticscholar.org/7b73a1f7b749d96305226a64d8644f96b92fe797 https://api.semanticscholar.org/9f981810441013520bd4c2e70b455025332ab134 https://api.semanticscholar.org/2550dfbbc2d73236aa20a6260c244b826c1a35d6 https://api.semanticscholar.org/fe14a04683dd695def544da7b1d3d797041c15f1 https://api.semanticscholar.org/55604c73022a40953ed0ff67c8b0e543ae6e9451 https://api.semanticscholar.org/972ef8223e22e0ede8f5ae17cf10edd780ff57f6 https://api.semanticscholar.org/09752443064819f865f3b094c61abe60431b14ee https://api.semanticscholar.org/3a15cf2594835dd2f0dab4b5cc47ac7eed3319d4 https://api.semanticscholar.org/193b7e753b97d7997a198cacd7c35c7c700d79be https://api.semanticscholar.org/40a6402789d2045b515e9c2bae483b5cb5f00155 https://api.semanticscholar.org/8919f2b1aad59b3c7a2cd4bcaf54de8a710b119c https://api.semanticscholar.org/dd1ef79f194d4ddb232c94b2dfe390b35ea301aa https://api.semanticscholar.org/d9754be54eeae6d731ed956ca785391b5c559227 https://api.semanticscholar.org/d3319d5d507a34c84ad706de9718790c8119f9c7 https://api.semanticscholar.org/b9358caf0a6909872f82bf918361569c1ca160dc https://api.semanticscholar.org/4f8a598d6c681604ca25f571c90d8534d9d12d6d https://api.semanticscholar.org/6c024b17ce6426a0ba85411d6d9ede0d397084f4 https://api.semanticscholar.org/fc027fccb19512a439fc17181c34ee1c3aad51b5 https://api.semanticscholar.org/a0b46d7ceef8d9f0ef87766bde81a4140190a085 https://api.semanticscholar.org/388358ad13be5199af58c7245aaa8f55d5520858 https://api.semanticscholar.org/c7780cff11068fecb322a43e459c56267a88aee7 https://api.semanticscholar.org/53e69513658c7f20c40e15b1640ba48327efeef6 https://api.semanticscholar.org/f3159fbd97282cbb0a139794b5b17341ecfb3ea2 https://api.semanticscholar.org/ba1046217f50c02c852c595a4fe81d1c1126d068 https://api.semanticscholar.org/71dd100ef9c731c31a566eecd598962f8a8dc24a https://api.semanticscholar.org/b3d59d2a9cd269300a835cf31dbc4076a139d944 https://api.semanticscholar.org/8134b59fc1770286447744d460cd9462267e15c8 https://api.semanticscholar.org/a73e37bda21208fc865803055f47837772a03bbd https://www.semanticscholar.org/paper/193b518bc3025804c6d587c74cbc154d91478417 https://www.semanticscholar.org/paper/03a5b2aac53443e6078f0f63b35d4f95d6d54c5d https://www.semanticscholar.org/paper/ade1b1bfe2abefbcfe324499aa284db2b8fc50a6 https://www.semanticscholar.org/paper/2777cd26b2c257843273fe41ad4c5b8cdf1b1b75 https://www.semanticscholar.org/paper/98bb60748eb8ef7a671cdd22faa87e377fd13060