Most speech recognition systems use spectral features based on fixed filters , such as MFCC and PLP . In this paper , we show that it is possible to achieve state of the art results by making the feature extractor a part of the network and jointly optimizing it with the rest of the network . The basic approach is to start with a convolutional layer that operates on the signal ( say , with a step size of 1.25 milliseconds ) , and aggregate the filter outputs over a portion of the time axis using a network in network architecture , and then down-sample to every 10 milliseconds for use by the rest of the network . Features are learned from word images , in an unsupervised manner , using a sliding window to extract horizontal patches . For two single writer historical data sets , it is shown that the proposed learned feature extractor outperforms two standard sets of features . The algorithm contains two parts : feature extractions and classification . The feature extractions are made of two regular features of arousal and one proposed feature ( fuzzy entropy ) . Fuzzy entropy highlights the possibilities of events . The analysis of results , after significant features were found using the OFS algorithm , shows that SVM is a better classification algorithm compared to GMM . The study also shows distinctions between emotions of males and females after feature extractions . We have applied a preprocessing to the cobwebs images in order to obtain only the valid information and compute the optimal size to reach the highest performance . We have used the principal component analysis ( PCA ) , independent component analysis ( ICA ) , Discrete Cosine Transform ( DCT ) , Wavelet Transform ( DWT ) and discriminative common vectors as features extractors , and proposed the fusion of several of them to improve the system 's performance . Finally , we have used the Least Square Vector Support Machine with radial basis function as a classifier . Breast cancer is the fifth most common cause of cancer death among women worldwide , even on Algeria that known about 12,000 new cases every year . Texture description has been a great interest in pattern recognition methods for looking deeper into features images , In this paper , we investigate the capability of the Local Binary Pattern texture and deep learning method for automated breast tumor images classification to be an efficient element for Computer aided diagnosis ( CAD ) system , where the extraction of meaningful information from the input image do not require features extractors . We have proposed a Convolution Neural Network ( CNN ) architecture based on LBP images as input after we compared their classification results by a standard CNN based on origin images as input . Meta - transfer learning ( MTL ) KwDataInput KwResultOutput Task distribution and corresponding dataset , learning rates , and Feature extractor , base learner , SS parameters Randomly initialize and samples in Evaluate by Eq . [ reference ] Optimize and by Eq . section : Network architecture As shown in Fig . 1 , the network consists of two parts : feature extraction , which computes a face descriptor for each input face image , and aggregation , which aggregates all face descriptors into a single compact template representation of the input image set . Feature extraction . Despite the recent progress made in the field , its applications in Computer-Aided Diagnosis ( CAD ) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features . Relevance Feedback ( RFB ) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images , which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy . In this chapter , the authors review some recent advances in RFB technology , and discuss its expanding role in content-based image retrieval from medical archives . The further development is looked up . These progresses include the improvement of the method of radiation force balance(RFB),such as a number of new derived formulas of radiation force and their application , which were tested and approved by colleagues of IEC , the expended application area of RFB to various focused beams from unfocused beam and the range up to 500W from 30W , and the developments of measuring hydrophones and of their calibration methods based on reciprocity principle , and the innovation on measurement techniques of acoustic power and field characteristics of high intensity focused ultrasound(HIFU ) as well as working out the first national standard of HIFU measurement in the world , promoting the standardization works on HIFU in IEC etc . Comp . , 68 ( 1999 ) , pp . 913 - 943 ) and the upscaling procedures which are very common in the engineering literature for solving this kind of partial differential equation . We also introduce a vari- ation of the RFB method , based on macrobubbles and referred to as the residual-free macrobubbles ( RFMB ) method , which gives more accurate numerical solutions . In the case of periodic coefficients we are able to prove a priori error estimates for the methods . This paper presents an error correction method known as restorative feedback ( RFB ) that provides error-correction for both permanent and temporal logic faults in any M-ary logic system . The RFB method is a variant of triple modular redundancy ( TMR ) , which achieves error correction in logic circuits by using three-fold redundancy . Unlike TMR , the RFB method has well-defined application to arbitrary M-ary logic systems as well as conventional binary logic circuits . The residual-free bubble method ( RFB ) is a parameter-free stable finite element method that has been successfully applied to a wide range of boundary-value problems exhibiting multiple-scale behaviour . If some local features of the solution are known a priori , the approximation properties of the RFB finite element space can be improved by enriching it on selected edges of the partition by edge-bubbles that are supported on pairs of neighbouring elements . This paper presents a method and circuit for correcting faults in logic systems . The method , called " restorative feedback " ( RFB ) , is similar in some respects to triple modular redundancy ( TMR ) , but has an improved error probability with respect to transient errors . Simulation results indicate an improvement by about two orders of magnitude compared to traditional TMR . Another issue on reduction of clients ' buffer demands is also important , especially for the goal to provide video services using limited-capability client devices such as PDAs and set-top boxes ( STBs ) . Related work includes the skyscraper broadcasting ( SkB ) , client-centric approach ( CCA ) , greedy disk-conserving broadcasting ( GDB ) , and reverse fast broadcasting ( RFB ) schemes . By combining RFS and RFB , this paper designs a hybrid broadcasting scheme ( HyB ) , which exhibits a good tradeoff between client buffering spaces and waiting time . It has been well recognized as an efficient approach for broadcasting popular videos by partitioning a video data stream into multiple segments and launching each segment through an individual channel simultaneously and periodically . Based on the design premises , some recent studies , including skyscraper broadcasting ( SkB ) , client-centric approach ( CCA ) , greedy disk-conserving broadcasting ( GDB ) , and reverse fast broadcasting ( RFB ) schemes , etc . , have been reported . To study the client segment downloading process , this paper first introduces an applicable sequence-based broadcasting model that can be used to minimize the required buffer size . 이를 이용하여 효과적인 동기식 협업 작업을 지원하는 시스템을 구성할 수 있다 . 본 논문에서는 Ultra VNC 프로그램과 RFB 프로토콜을 확장하여 같은 시간에 일어나는 인터넷 기반 The application service system has been greatly developed and used in recent years . This paper makes an introduction to current situation of Virtual Network Computing ( VNC ) technology and RFB ( Remote Frame Buffer ) Protocol , and the inadequacy of VNC technology and RFB Protocol . In order to make it more suitable for the Enterprise application service system , we study out an IAP Protocol ( Intrusion Alert Protocol ) based on RFB . In this paper we describe the problem of optimizing two fairness measures : the Relative Fairness Bound ( RFB ) and the Absolute Fairness Bound ( AFB ) , which are used in the packet-switched networks to evaluate the fairness of the packet scheduling disciplines . In our work we use the apportionment theory to prove some properties of the RFB measure . We examine the properties of the AFB measure on the basis of transformation to the Product Rate Variation problem considered in the context of Just-in-Time production systems . Owing to complex configurations and the multi-physics phenomenon in a Redox flow battery ( RFB ) , designing an RFB flow frame through Computational fluid dynamics ( CFD ) or experimentally requires high design costs . As an alternative approach , a compartment model can be applied to the design of an optimal RFB flow frame . In this study , we developed a pre-design model to estimate the parameters related to the energy efficiency of an RFB . Using nonlinear Radial Basis Function kernels in a support vector machine , we can explain the responses of some of these cells better than standard linear and phase-invariant complex cell models . This suggests that V1 neurons exhibit more complex and diverse responses than standard models can capture , ranging from simple and complex cells strongly driven by their classical receptive fields , to cells with more nonlinear receptive fields inferred from the nonparametric and RFB model , and cells that are not visually responsive despite robust firing . The receptive field concept is described , along with how this concept has been applied to retinal ganglion cells . A brief description of different types of retinal ganglion cell receptive fields is given and an outline provided of widely accepted receptive field models . The various terminologies in use for naming different classes of retinal ganglion cells and their receptive field structures is discussed , and a summary of the current state of this field of research is presented . Recent data on the dynamics of orientation tuning , obtained by a cross-correlation technique , may help to distinguish between these classes of models . To test this possibility , we simulated the measurement of orientation tuning dynamics on various receptive field models , including a simple Hubel-Wiesel type feedforward model : a linear spatiotemporal filter followed by an integrate-and-fire spike generator . The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells . To this end , we analyse the receptive field ( RF ) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models . Using our receptive field analysis , we adapt both ResNet and DenseNet , achieving state-of-theart performance and eventually outperforming the VGG-based models . We introduce systematic ways of adapting the RF in CNNs , and present results on three data sets that show how changing the RF over the time and frequency dimensions affects a model ’s performance . The transform captures the image data in that it is injective . Using this starting point , we present a continuous analog of the four stage edge detection breakdown by Bezdek et al. , and arrive at a framework for casting many kinds of image processing algorithms in a biologically plausible manner : as feed forward , receptive field based algorithms using known operations . We show how this leads to an optimization scheme for Radon transform based algorithms and show the results of applying this theory to biologically plausible algorithms for motion and color processing . Yet , relatively little is known about their effects on the neural processing of visual inputs . Here we examine this question in primary visual cortex ( V1 ) using receptive-field-based models , combined with an experimental design that leaves the retinal stimulus unaffected by saccades . This approach allows us to analyse V1 stimulus processing during saccades with unprecedented detail , revealing robust perisaccadic modulation . Some illustrations also discuss the major findings of cortical neurophysiology . However , if it is true that the analytical framework for understanding the neural network of the cerebral cortex requires a new organizing principle , then the receptive field concept turns out to have been only a first step in the direction of understanding the true complexity of the brain . We would like to discuss the analysis of CR and the mechanism of MCS in the phantom-limb pain . S3P-H3 Thalamic sensory relay nucleus stimulation and phantom limb pain Yoichi Katayama Department of Neurological Surgery , Nihon University , Tokyo , Japan The primary somatosensory cortex undergoes reorganization of receptive field representation in patients with phantom limb pain ( PP ) . The extent of cortical reorganization and the degree of PP have been reported to be correlated . However , the gross organization of spectrotemporal processing of speech within the STG is not well characterized . Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG . These findings demonstrate robust functional organization based on spectrotemporal modulation content , and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli . Both single- and multi-step forecasting performances are studied in the context of the sunspot series . Additionally , a heuristic for selecting the placement and dilations of the receptive field functions is presented . The performance of multi-layered perceptron networks trained using the data arising from the encoding scheme is assessed . The methodology of calibration compares the measured data obtained from tactile sensor with the one from a load cell fixed on the stimulator . The procedure of receptive-field measurement for tactile sensor on the prosthetic hand is shown and the equation for estimating the position and amplitude of surface load on top of elastic cover is established . Different combinations of tactile sensors are implemented on the rigid substance and two motion patterns of stimulator are performed on them , which are indentation and sliding . Despite these advances in existing designs , there is still room for improvement in such areas as large die sizes , device defects and low vielcls , and the high power consumption of existing circuits . As a way of overcoming these limitations a novel smart image sensor design is presented , based on the receptive field paradigm . The receptive field circuits consist ot several photoreceptors connected by preprocessing circuitry , able to perform contrast enhancement and Gaussian smoothing , as well as more specialized tasks such as edge detection and motion detection . A recent effort using more complex pooling operations , spatial pyramid pooling , is mainly designed to deal with images of varying size , rather than delving in to different pooling functions or incorporating learning . Learning pooling functions is analogous to receptive field learning . However methods like lead to a more difficult learning procedure that in turn leads to a less competitive result , e.g. an error rate of on unaugmented CIFAR10 . [ t ! ] ReceptiveField : Create Receptive Field { algorithmic} [ 1 ] vertex v , graph labeling ℓ , receptive field size k Gnorm We have the following result concerning the complexity of the optimal normalization problem . Our experiments show the effectiveness of IMP on both clothing parsing ( with complex layering , large deformations , and non-convex objects ) , and on street scene segmentation ( with many overlapping instances and small objects ) . On the Varied Clothing Parsing dataset ( VCP ) , we show instance mask projection can improve mIOU by 3 points over a state-of-the-art Panoptic FPN segmentation approach . On the ModaNet clothing parsing dataset , we show a dramatic improvement of 20.4 % compared to existing baseline semantic segmentation results .
Most speech recognition systems use spectral features based on fixed filters , such as MFCC and PLP . In this paper , we show that it is possible to achieve state of the art results by making the feature extractor a part of the network and jointly optimizing it with the rest of the network . The basic approach is to start with a convolutional layer that operates on the signal ( say , with a step size of 1.25 milliseconds ) , and aggregate the filter outputs over a portion of the time axis using a network in network architecture , and then down-sample to every 10 milliseconds for use by the rest of the network .
Features are learned from word images , in an unsupervised manner , using a sliding window to extract horizontal patches . For two single writer historical data sets , it is shown that the proposed learned feature extractor outperforms two standard sets of features .
The algorithm contains two parts : feature extractions and classification . The feature extractions are made of two regular features of arousal and one proposed feature ( fuzzy entropy ) . Fuzzy entropy highlights the possibilities of events .
The analysis of results , after significant features were found using the OFS algorithm , shows that SVM is a better classification algorithm compared to GMM . The study also shows distinctions between emotions of males and females after feature extractions .
We have applied a preprocessing to the cobwebs images in order to obtain only the valid information and compute the optimal size to reach the highest performance . We have used the principal component analysis ( PCA ) , independent component analysis ( ICA ) , Discrete Cosine Transform ( DCT ) , Wavelet Transform ( DWT ) and discriminative common vectors as features extractors , and proposed the fusion of several of them to improve the system 's performance . Finally , we have used the Least Square Vector Support Machine with radial basis function as a classifier .
Breast cancer is the fifth most common cause of cancer death among women worldwide , even on Algeria that known about 12,000 new cases every year . Texture description has been a great interest in pattern recognition methods for looking deeper into features images , In this paper , we investigate the capability of the Local Binary Pattern texture and deep learning method for automated breast tumor images classification to be an efficient element for Computer aided diagnosis ( CAD ) system , where the extraction of meaningful information from the input image do not require features extractors . We have proposed a Convolution Neural Network ( CNN ) architecture based on LBP images as input after we compared their classification results by a standard CNN based on origin images as input .
Meta - transfer learning ( MTL ) KwDataInput KwResultOutput Task distribution and corresponding dataset , learning rates , and Feature extractor , base learner , SS parameters Randomly initialize and samples in Evaluate by Eq . [ reference ] Optimize and by Eq .
section : Network architecture As shown in Fig . 1 , the network consists of two parts : feature extraction , which computes a face descriptor for each input face image , and aggregation , which aggregates all face descriptors into a single compact template representation of the input image set . Feature extraction .
Despite the recent progress made in the field , its applications in Computer-Aided Diagnosis ( CAD ) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features . Relevance Feedback ( RFB ) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images , which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy . In this chapter , the authors review some recent advances in RFB technology , and discuss its expanding role in content-based image retrieval from medical archives .
The further development is looked up . These progresses include the improvement of the method of radiation force balance(RFB),such as a number of new derived formulas of radiation force and their application , which were tested and approved by colleagues of IEC , the expended application area of RFB to various focused beams from unfocused beam and the range up to 500W from 30W , and the developments of measuring hydrophones and of their calibration methods based on reciprocity principle , and the innovation on measurement techniques of acoustic power and field characteristics of high intensity focused ultrasound(HIFU ) as well as working out the first national standard of HIFU measurement in the world , promoting the standardization works on HIFU in IEC etc .
Comp . , 68 ( 1999 ) , pp . 913 - 943 ) and the upscaling procedures which are very common in the engineering literature for solving this kind of partial differential equation . We also introduce a vari- ation of the RFB method , based on macrobubbles and referred to as the residual-free macrobubbles ( RFMB ) method , which gives more accurate numerical solutions . In the case of periodic coefficients we are able to prove a priori error estimates for the methods .
This paper presents an error correction method known as restorative feedback ( RFB ) that provides error-correction for both permanent and temporal logic faults in any M-ary logic system . The RFB method is a variant of triple modular redundancy ( TMR ) , which achieves error correction in logic circuits by using three-fold redundancy . Unlike TMR , the RFB method has well-defined application to arbitrary M-ary logic systems as well as conventional binary logic circuits .
The residual-free bubble method ( RFB ) is a parameter-free stable finite element method that has been successfully applied to a wide range of boundary-value problems exhibiting multiple-scale behaviour . If some local features of the solution are known a priori , the approximation properties of the RFB finite element space can be improved by enriching it on selected edges of the partition by edge-bubbles that are supported on pairs of neighbouring elements .
This paper presents a method and circuit for correcting faults in logic systems . The method , called " restorative feedback " ( RFB ) , is similar in some respects to triple modular redundancy ( TMR ) , but has an improved error probability with respect to transient errors . Simulation results indicate an improvement by about two orders of magnitude compared to traditional TMR .
Another issue on reduction of clients ' buffer demands is also important , especially for the goal to provide video services using limited-capability client devices such as PDAs and set-top boxes ( STBs ) . Related work includes the skyscraper broadcasting ( SkB ) , client-centric approach ( CCA ) , greedy disk-conserving broadcasting ( GDB ) , and reverse fast broadcasting ( RFB ) schemes . By combining RFS and RFB , this paper designs a hybrid broadcasting scheme ( HyB ) , which exhibits a good tradeoff between client buffering spaces and waiting time .
It has been well recognized as an efficient approach for broadcasting popular videos by partitioning a video data stream into multiple segments and launching each segment through an individual channel simultaneously and periodically . Based on the design premises , some recent studies , including skyscraper broadcasting ( SkB ) , client-centric approach ( CCA ) , greedy disk-conserving broadcasting ( GDB ) , and reverse fast broadcasting ( RFB ) schemes , etc . , have been reported . To study the client segment downloading process , this paper first introduces an applicable sequence-based broadcasting model that can be used to minimize the required buffer size .
이를 이용하여 효과적인 동기식 협업 작업을 지원하는 시스템을 구성할 수 있다 . 본 논문에서는 Ultra VNC 프로그램과 RFB 프로토콜을 확장하여 같은 시간에 일어나는 인터넷 기반
The application service system has been greatly developed and used in recent years . This paper makes an introduction to current situation of Virtual Network Computing ( VNC ) technology and RFB ( Remote Frame Buffer ) Protocol , and the inadequacy of VNC technology and RFB Protocol . In order to make it more suitable for the Enterprise application service system , we study out an IAP Protocol ( Intrusion Alert Protocol ) based on RFB .
In this paper we describe the problem of optimizing two fairness measures : the Relative Fairness Bound ( RFB ) and the Absolute Fairness Bound ( AFB ) , which are used in the packet-switched networks to evaluate the fairness of the packet scheduling disciplines . In our work we use the apportionment theory to prove some properties of the RFB measure . We examine the properties of the AFB measure on the basis of transformation to the Product Rate Variation problem considered in the context of Just-in-Time production systems .
Owing to complex configurations and the multi-physics phenomenon in a Redox flow battery ( RFB ) , designing an RFB flow frame through Computational fluid dynamics ( CFD ) or experimentally requires high design costs . As an alternative approach , a compartment model can be applied to the design of an optimal RFB flow frame . In this study , we developed a pre-design model to estimate the parameters related to the energy efficiency of an RFB .
Using nonlinear Radial Basis Function kernels in a support vector machine , we can explain the responses of some of these cells better than standard linear and phase-invariant complex cell models . This suggests that V1 neurons exhibit more complex and diverse responses than standard models can capture , ranging from simple and complex cells strongly driven by their classical receptive fields , to cells with more nonlinear receptive fields inferred from the nonparametric and RFB model , and cells that are not visually responsive despite robust firing .
The receptive field concept is described , along with how this concept has been applied to retinal ganglion cells . A brief description of different types of retinal ganglion cell receptive fields is given and an outline provided of widely accepted receptive field models . The various terminologies in use for naming different classes of retinal ganglion cells and their receptive field structures is discussed , and a summary of the current state of this field of research is presented .
Recent data on the dynamics of orientation tuning , obtained by a cross-correlation technique , may help to distinguish between these classes of models . To test this possibility , we simulated the measurement of orientation tuning dynamics on various receptive field models , including a simple Hubel-Wiesel type feedforward model : a linear spatiotemporal filter followed by an integrate-and-fire spike generator . The computational study reveals that simple feedforward models may account for some aspects of the experimental data but fail to explain many salient features of orientation tuning dynamics in V1 cells .
To this end , we analyse the receptive field ( RF ) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models . Using our receptive field analysis , we adapt both ResNet and DenseNet , achieving state-of-theart performance and eventually outperforming the VGG-based models . We introduce systematic ways of adapting the RF in CNNs , and present results on three data sets that show how changing the RF over the time and frequency dimensions affects a model ’s performance .
The transform captures the image data in that it is injective . Using this starting point , we present a continuous analog of the four stage edge detection breakdown by Bezdek et al. , and arrive at a framework for casting many kinds of image processing algorithms in a biologically plausible manner : as feed forward , receptive field based algorithms using known operations . We show how this leads to an optimization scheme for Radon transform based algorithms and show the results of applying this theory to biologically plausible algorithms for motion and color processing .
Yet , relatively little is known about their effects on the neural processing of visual inputs . Here we examine this question in primary visual cortex ( V1 ) using receptive-field-based models , combined with an experimental design that leaves the retinal stimulus unaffected by saccades . This approach allows us to analyse V1 stimulus processing during saccades with unprecedented detail , revealing robust perisaccadic modulation .
Some illustrations also discuss the major findings of cortical neurophysiology . However , if it is true that the analytical framework for understanding the neural network of the cerebral cortex requires a new organizing principle , then the receptive field concept turns out to have been only a first step in the direction of understanding the true complexity of the brain .
We would like to discuss the analysis of CR and the mechanism of MCS in the phantom-limb pain . S3P-H3 Thalamic sensory relay nucleus stimulation and phantom limb pain Yoichi Katayama Department of Neurological Surgery , Nihon University , Tokyo , Japan The primary somatosensory cortex undergoes reorganization of receptive field representation in patients with phantom limb pain ( PP ) . The extent of cortical reorganization and the degree of PP have been reported to be correlated .
However , the gross organization of spectrotemporal processing of speech within the STG is not well characterized . Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG . These findings demonstrate robust functional organization based on spectrotemporal modulation content , and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli .
Both single- and multi-step forecasting performances are studied in the context of the sunspot series . Additionally , a heuristic for selecting the placement and dilations of the receptive field functions is presented . The performance of multi-layered perceptron networks trained using the data arising from the encoding scheme is assessed .
The methodology of calibration compares the measured data obtained from tactile sensor with the one from a load cell fixed on the stimulator . The procedure of receptive-field measurement for tactile sensor on the prosthetic hand is shown and the equation for estimating the position and amplitude of surface load on top of elastic cover is established . Different combinations of tactile sensors are implemented on the rigid substance and two motion patterns of stimulator are performed on them , which are indentation and sliding .
Despite these advances in existing designs , there is still room for improvement in such areas as large die sizes , device defects and low vielcls , and the high power consumption of existing circuits . As a way of overcoming these limitations a novel smart image sensor design is presented , based on the receptive field paradigm . The receptive field circuits consist ot several photoreceptors connected by preprocessing circuitry , able to perform contrast enhancement and Gaussian smoothing , as well as more specialized tasks such as edge detection and motion detection .
A recent effort using more complex pooling operations , spatial pyramid pooling , is mainly designed to deal with images of varying size , rather than delving in to different pooling functions or incorporating learning . Learning pooling functions is analogous to receptive field learning . However methods like lead to a more difficult learning procedure that in turn leads to a less competitive result , e.g. an error rate of on unaugmented CIFAR10 .
[ t ! ] ReceptiveField : Create Receptive Field { algorithmic} [ 1 ] vertex v , graph labeling ℓ , receptive field size k Gnorm We have the following result concerning the complexity of the optimal normalization problem .
Our experiments show the effectiveness of IMP on both clothing parsing ( with complex layering , large deformations , and non-convex objects ) , and on street scene segmentation ( with many overlapping instances and small objects ) . On the Varied Clothing Parsing dataset ( VCP ) , we show instance mask projection can improve mIOU by 3 points over a state-of-the-art Panoptic FPN segmentation approach . On the ModaNet clothing parsing dataset , we show a dramatic improvement of 20.4 % compared to existing baseline semantic segmentation results .
Acoustic Modelling from the Signal Domain Using CNNs Keyword Spotting with Convolutional Deep Belief Networks and Dynamic Time Warping Automated detection of arousal event with fuzzy entropy using physiological signals Study of prosodic feature extraction for multidialectal Odia speech emotion recognition Spider specie identification and verification based on pattern recognition of it cobweb Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification Meta-Transfer Learning for Few-Shot Learning GhostVLAD for set-based face recognition Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases Thirty years of ultrasonic measurement and standardization in China Capturing Small Scales in Elliptic Problems Using a Residual-Free Bubbles Finite Element Method Error Correction via Restorative Feedback in M-ary Logic Circuits Enhanced RFB method An Error Correction Method for Binary and Multiple-Valued Logic Hybrid Broadcasting With Small Buffer Demand and Waiting Time for Video-on-Demand Applications Generalized Sequence-Based and Reverse Sequence-Based Models for Broadcasting Hot Videos CoVNC : 효과적인 협업을 위한 VNC의 확장 Intelligent Application Service System Based on VNC Fairness of schedules in the control of packet-switched networks Pre-design model for redox flow battery design Testing our conceptual understanding of V1 function Retinal Ganglion Cells: Receptive Fields Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification Deriving and combining biologically plausible visual processes with the windowed Radon transform Saccadic modulation of stimulus processing in primary visual cortex The Receptive Fields of Visual Neurons Cerebral reorganization by adaptable robot hand Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli Ensemble encoding for time series forecasting with MLP networks A Mechatronic Platform for Calibration and Performance Test of Extrinsic Tactile Sensor on Prosthesis Hand Integrated smart image sensors in digital CMOS technology Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Learning Convolutional Neural Networks for Graphs IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things
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INTERSPEECH ICANN (2) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ACCV ISMVL Human Vision and Electronic Imaging AISTATS ICML
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