The SNIP program is a FORTRAN computer code that generates NASTRAN structural model thermal loads when given SINDA ( or similar thermal model ) temperature results . SNIP correlates thermal nodes to structural elements to interface SINDA finite difference thermal models with NASTRAN finite element structural models . The program outputs NASTRAN element temperature load cards for each element and NASTRAN case control cards for each temperature load set . SNIP also outputs a list of elements that contains the numbers of the SINDA nodes related to each NASTRAN element and the weight that is given to each node in temperature calculations . SNIP is written in ANSI standard FORTRAN 77 . The results revealed an uneven spread of OA journals across disciplines , ranged from 5.5 to 28.7 per cent . Studying the quality of journals as measured by CiteScore , SJR SNIP leads us to the finding that , in all research areas , except for health profession and nursing , non-OA journals attain statistically significant higher average quality than do OA journals . Originality/value Although OA publishing improves the visibility of scholarly journals , this increase is not always coupled with increase in journals ’ impact and quality . The development was started using the Tersus OEM GNSS board series BX305 , including the integration of the board to a mini PC . Further developments have been made covering the following , namely : i ) to add a long-term stable power supply in the receiver , ii ) customization of receiver command settings , iii ) to add a GSM modem and internet protocol for data communication from the receiver to a cloud server , iv ) installation of SNIP software in a cloud server to provide CORS corrections with NTRIP caster , and v ) testing the NTRIP client performance in the rover receiver . Results from the land cadastre surveys carried out in Bandung , Padang and Tangerang Cities show that the length of time for the fixed positioning with resolved ambiguities could be obtained within less than 5 - 7 seconds with the rover locations less than 10 km from the base station . Then , based on the model semantics , the procedure for generating fault trees by model checking is established . Finally , using temporal logic to describe the system safety attributes , we adopt the model checking tool SNIP to verify the safety attributes and generate the fault tree automatically . The fault modeling method that is proposed in this paper includes the inherent constraints between faults , which makes the system fault model more realistic and accurate . The advent of social media has enabled us to explore the impact of academic entities beyond the conventional bibliometric community . The traditional bibliometric indicators such as citation count , h-index and SNIP metrics aim to represent the propagation of knowledge in the academic world rather than the impact of the research on the wider world . In this study , we are interested in measuring the impact of the academic world on social media . Therefore , this work proposes the new method : the Social Network Intelligence Process ( SNIP ) Method . The SNIP Method and its related management information items were validated through a series of expert interviews and an in-depth single case study at the leading Dutch social network site . Variability in software product lines is generally expressed in terms of features , and the number of potential products is exponential in the number of features . Whereas classical model checkers are only capable of checking properties against each individual product in the product line , SNIP exploits specifically designed algorithms to check all products in a single step . This is done by using a concise mathematical structure for product line behaviour , that exploits similarities and represents the behaviour of all products in a compact manner . First , we introduce a new DEF that is significantly more compact than the standard DEF . Second , we propose a new path-based formulation of SNIP . The number of constraints required to define this formulation grows exponentially with the size of the network , but the model can be solved via delayed constraint generation . Obstructive diseases are defined by a reduced FEV1/VC ratio . Third , neuromuscular diseases , mainly represented by DMD and SMA , are associated with a decrease in vital capacity linked to respiratory muscle weakness that is better detected by PImax , PEmax and SNIP measurements . The difference between SSH and S DF is that in SSH , anchors with two neighboring sizes shared the same detection feature map , while in S DF , anchors with different sizes are attached to different detection feature maps . SNIP discussed an alternative approach to handle scales . It showed that CNNs are not robust to changes in scale , so training and testing on the same scales of an image pyramid can be a more optimal strategy . A new technique , SNIPER , is proposed for learning a model that deals with continuous values of exceptionality . Specifically , given some training objects associated with a continuous attribute F , SNIPER induces a rule-based model for the identification of those objects likely to score the maximum values for F. The purpose of SNIPER differs from the one pursued in regression problems , since its main objective is to retrieve those objects more likely to score the highest values of F. Although there are opportunities for improvement , the results of a preliminary evaluation are encouraging . SNIPER is competitive in the quality of the attained results with respect to some established competitors , while outperforming them when the exceptional objects are very rare . We present SNIPER , an algorithm for performing efficient multi-scale training in instance level visual recognition tasks . Instead of processing every pixel in an image pyramid , SNIPER processes context regions around ground-truth instances ( referred to as chips ) at the appropriate scale . This adds a new challenge to the DSE process . In this paper , using the multi-objective design space exploration tool FADSE , we tried to optimize the hardware and software parameters of the multicore SNIPER simulator running SPLASH-2 benchmarks suite . We optimized the hardware parameters ( nr cores , cache sizes , cache associativity , etc . ) and software parameters ( GCC optimizations , threads , and scheduler ) values that have been varied during the DSE process and shown the impact of these parameters on the optimization 's multi-objectives ( performance , area and power consumption ) . The framework is developed for a population of n interconnected neurons . Several well-known neuron models are studied within the framework , including both excitatory and inhibitory simplified Hodgkin-Huxley , Hopf , and SNIPER models . For each model , we characterize the sets that the solutions to each system converge to . We discuss digital forensics , digital steganography , and intrusion detection ( the use of learning techniques , the detection of masquerading , and honeypots for trapping intruders ) . Case studies include , for example : investigating Internet auction fraud with NetProbe ; graph mining for malware detection with Polonium ; link analysis with Coplink ; a project of the U.S. Federal Defense Financial Accounting Service ; information extraction tools for integration with a link analysis tool ; the Poznan ontology model for the link analysis of fuel fraud ; and fiscal fraud detection with the Pisa SNIPER project . In this work , we consider a generic model for a saddle-node bifurcation on a limit cycle representative for neuron excitability type-I. It is given by N nonlocally coupled SNIPER oscillators in the oscillatory regime arranged on a ring . Depending on the system parameters we obtain chimera states with multiple coherent regions ( clustered chimeras ) , coexisting traveling waves , and we observe a flip in the mean phase velocities of the coherent and incoherent regions . We use a generic model for type-I excitability ( known as the SNIPER or SNIC model ) to describe the local dynamics of nodes within a network in the presence of non-zero coupling delays . Utilising the method of the Master Stability Function , we investigate the stability of the zero-lag synchronised dynamics of the network nodes and its dependence on the two coupling parameters , namely the coupling strength and delay time . The saccades in all these R - CNN variants are thus single - type and single - object , in that there is a single type of processing of crops , and each crop produces at most a single object . AutoFocus , which builds upon SNIPER that improved R - CNN training , adds a branch to Faster R - CNN to predict the regions that are likely to contain small objects . Then it applies Faster R - CNN again to each of those regions by cropping the original pixels . document : SNIPER : Efficient Multi - Scale Training We present SNIPER , an algorithm for performing efficient multi - scale training in instance level visual recognition tasks . This paper also states the applications of this technology along with its advantages and various disadvantages . It uses a multi-scale training method that can run at various sizes offering an excellent relation speed and accuracy . We have based our model on yolo v2 all the while bringing our own changes for shifting from object detection to Face identification . Specifically , we train our basketball detection model based on the Region-based Fully Convolutional Networks ( R-FCN ) which uses the fully convolutional Residual Network ( ResNet ) as the backbone network . What 's more , we apply some new techniques including Online Hard Example Mining ( OHEM ) , Soft-NMS and multi-scale training strategy to achieve higher detection accuracy . In detail , the OHEM method can reduce the cost of fine-tuning during training by calculating the loss of the RoIs . However , there can be no doubt that inspecting defects on steel surface is always a data-limited task due to difficult sample collection and expensive expert labeling . Unlike the previous works in which only labeled samples are treated using supervised classifiers , we propose a semi-supervised learning ( SSL ) defect classification approach based on multi-training of two different networks : a categorized generative adversarial network ( GAN ) and a residual network . This method uses the GAN to generate a large number of unlabeled samples . Named Entity Recognition ( NER ) for Chinese corpus such as social media text and medical records is a grand chanllenge as the entity boundary is not easy to be accurately clarified . In this work , we describe and evaluate a character-level tagger for Chinese NER , which incorporates multitask learning , self-attention and multi-step training methods to exploit richer features and further improve the model performance . The proposed model has achieved 90.52 % strict F1 on the Electronic medical records dataset ( CCKS-NER 2017 ) , which is the best single model at present . We propose a multi-step training method for designing generalized linear classifiers . First , an initial multi-class linear classifier is found through regression . Our approach alleviates the paired training constraint by introducing a physical-model based disentanglement and reconstruction mechanism . A multi-scale adversarial training is employed to generate perceptually haze-free images . Experimental results on synthetic datasets demonstrate our superior performance compared with the state-ofthe-art methods in terms of PSNR , SSIM and CIEDE2000 . In this paper , we present a novel depth map inpainting method for Kinect with a multi-scale deep Convolutional Neural Network ( CNN ) . This method has three stages : depth map pre-processing , multi-scale network training and image optimization . Our method provides a possibility to make a comprehensive refinement for Kinect depth map , including denoising , filling up small holes and inpainting large areas of depth missing . In this network , reverse connected structure enriches the semantic context information of previous layers , while separable convolution is introduced for sparse representation of heavy feature maps generated from subnetworks . Further , we use multi-scale training scheme , online hard example mining , and model compression technique to accelerate the training process as well as reduce the parameters . Experimental results on Pascal Visual Object Classes ( VOC ) 2007 + 2012 and MicroSoft Common Objects in COntext ( MS COCO ) 2014 demonstrate the proposed method yields state-of-the-art performance . Scale variation remains a challenge problem for object detection . Common paradigms usually adopt multi-scale training & testing ( image pyramid ) or FPN ( feature pyramid network ) to process objects in wide scale range . However , multi-scale methods aggravate more variation of scale that even deep convolution neural networks with FPN can not handle well . Multi-times training is the tendency used in the middle-distance training all over the world , in the three years ' practice , we have made a satisfactory achiement . The invention discloses an image quality testing method based on a deep convolutional neural network . The image quality testing method based on the deep convolutional neural network comprises the following steps of firstly , establishing a sample set , establishing a deep convolutional neural network model , training the deep convolutional neural network model under different initial conditions , connecting the optimal deep convolutional neural network models obtained through the multiple times of training in parallel to obtain an image quality testing system , and using the obtained image quality testing system for testing images to be tested . According to the image quality testing method based on the deep convolutional neural network , characteristic learning is carried out by simulating the learning process of human brains , the problem that characteristics are difficult to select in an existing image quality testing method is solved , the contingency of predicting results is reduced , integration is high , generalization ability is strong , and testing effects are good . Our single - scale trained detector has already achieved a result of , which outperforms the R - FCN by 3.9 points . In addition , the multi - scale training further improves the performance up to . Interestingly , we observed that the more challenging the dataset , the more the promotion ( , for VOC07 , for VOC12 and for COCO , all in multi - scale training ) , which directly proves that our approach can effectively cope with a variety of complex situations . Hence , the number of chips generated per image during training adaptively changes based on the scene complexity . SNIPER only processes 30 % more pixels compared to the commonly used single scale training at 800x1333 pixels on the COCO dataset . But , it also observes samples from extreme resolutions of the image pyramid , like 1400x2000 pixels . For example , constructing a scale image pyramid ( e.g. scales= x , x , x ) requires processing times the number of pixels present in the original image . For this reason , it is impractical to use multi - scale training in many scenarios . Recently , it is shown that ignoring gradients of objects which are of extreme resolutions is beneficial while using multiple scales during training . If we do not include regions in the image containing negatives which are similar in appearance to positive instances , it would increase our false positive rate and adversely affect detection performance . SNIPER is an efficient multi - scale training algorithm . In all experiments in this paper we use the aforementioned three scales ( See Section [ reference ] for the details ) . Training with variable - size images increases scale - invariance and reduces over - fitting . We develop a simple multi - size training method . For a single network to accept variable input sizes , we approximate it by multiple networks that share all parameters , while each of these networks is trained using a fixed input size . In each epoch we train the network with a given input size , and switch to another input size for the next epoch . Experiments show that this multi - size training converges just as the traditional single - size training , and leads to better testing accuracy . The advantages of SPP are orthogonal to the specific CNN designs . This result is similar to the 50 - bin pyramid above ( 34.98 / 14.14 ) , but considerably better than the no - SPP counterpart ( 35.99 / 14.76 ) . subsubsection : Multi - size Training Improves Accuracy Table [ reference ] The VGG16 SPPnet results were computed by the authors of [ reference ] . SPPnet uses five scales during both training and testing . The improvement of Fast R - CNN over SPPnet illustrates that even though Fast R - CNN uses single - scale training and testing , fine - tuning the conv layers provides a large improvement in mAP ( from 63.1 % to 66.9 % ) . R - CNN achieves a mAP of 66.0 % . Yet it achieves a mAP of 66.9 % , which is slightly higher than the 66.0 % reported for R - CNN [ reference ] , even though R - CNN uses " infinite " scales in the sense that each proposal is warped to a canonical size . Since single - scale processing offers the best tradeoff between speed and accuracy , especially for very deep models , all experiments outside of this sub - section use single - scale training and testing with s = 600 pixels . section : Do we need more training data ? Second , we propose to use position - sensitive average pooling instead of normal average pooling for the last feature voting in R - FCN , which leads to an improved embedding . Third , multi - scale training strategy and on - line Hard Example Mining ( OHEM ) strategy are adopted for training . In the testing phase , we also ensemble the multi - scale detection results to improve the performance . Unless noted , their default settings were used . End - to - end training was used instead of multi - step training . section : Faster - RCNN :
The SNIP program is a FORTRAN computer code that generates NASTRAN structural model thermal loads when given SINDA ( or similar thermal model ) temperature results . SNIP correlates thermal nodes to structural elements to interface SINDA finite difference thermal models with NASTRAN finite element structural models .
The program outputs NASTRAN element temperature load cards for each element and NASTRAN case control cards for each temperature load set . SNIP also outputs a list of elements that contains the numbers of the SINDA nodes related to each NASTRAN element and the weight that is given to each node in temperature calculations . SNIP is written in ANSI standard FORTRAN 77 .
The results revealed an uneven spread of OA journals across disciplines , ranged from 5.5 to 28.7 per cent . Studying the quality of journals as measured by CiteScore , SJR SNIP leads us to the finding that , in all research areas , except for health profession and nursing , non-OA journals attain statistically significant higher average quality than do OA journals . Originality/value Although OA publishing improves the visibility of scholarly journals , this increase is not always coupled with increase in journals ’ impact and quality .
The development was started using the Tersus OEM GNSS board series BX305 , including the integration of the board to a mini PC . Further developments have been made covering the following , namely : i ) to add a long-term stable power supply in the receiver , ii ) customization of receiver command settings , iii ) to add a GSM modem and internet protocol for data communication from the receiver to a cloud server , iv ) installation of SNIP software in a cloud server to provide CORS corrections with NTRIP caster , and v ) testing the NTRIP client performance in the rover receiver . Results from the land cadastre surveys carried out in Bandung , Padang and Tangerang Cities show that the length of time for the fixed positioning with resolved ambiguities could be obtained within less than 5 - 7 seconds with the rover locations less than 10 km from the base station .
Then , based on the model semantics , the procedure for generating fault trees by model checking is established . Finally , using temporal logic to describe the system safety attributes , we adopt the model checking tool SNIP to verify the safety attributes and generate the fault tree automatically . The fault modeling method that is proposed in this paper includes the inherent constraints between faults , which makes the system fault model more realistic and accurate .
The advent of social media has enabled us to explore the impact of academic entities beyond the conventional bibliometric community . The traditional bibliometric indicators such as citation count , h-index and SNIP metrics aim to represent the propagation of knowledge in the academic world rather than the impact of the research on the wider world . In this study , we are interested in measuring the impact of the academic world on social media .
Therefore , this work proposes the new method : the Social Network Intelligence Process ( SNIP ) Method . The SNIP Method and its related management information items were validated through a series of expert interviews and an in-depth single case study at the leading Dutch social network site .
Variability in software product lines is generally expressed in terms of features , and the number of potential products is exponential in the number of features . Whereas classical model checkers are only capable of checking properties against each individual product in the product line , SNIP exploits specifically designed algorithms to check all products in a single step . This is done by using a concise mathematical structure for product line behaviour , that exploits similarities and represents the behaviour of all products in a compact manner .
First , we introduce a new DEF that is significantly more compact than the standard DEF . Second , we propose a new path-based formulation of SNIP . The number of constraints required to define this formulation grows exponentially with the size of the network , but the model can be solved via delayed constraint generation .
Obstructive diseases are defined by a reduced FEV1/VC ratio . Third , neuromuscular diseases , mainly represented by DMD and SMA , are associated with a decrease in vital capacity linked to respiratory muscle weakness that is better detected by PImax , PEmax and SNIP measurements .
The difference between SSH and S DF is that in SSH , anchors with two neighboring sizes shared the same detection feature map , while in S DF , anchors with different sizes are attached to different detection feature maps . SNIP discussed an alternative approach to handle scales . It showed that CNNs are not robust to changes in scale , so training and testing on the same scales of an image pyramid can be a more optimal strategy .
A new technique , SNIPER , is proposed for learning a model that deals with continuous values of exceptionality . Specifically , given some training objects associated with a continuous attribute F , SNIPER induces a rule-based model for the identification of those objects likely to score the maximum values for F. The purpose of SNIPER differs from the one pursued in regression problems , since its main objective is to retrieve those objects more likely to score the highest values of F. Although there are opportunities for improvement , the results of a preliminary evaluation are encouraging . SNIPER is competitive in the quality of the attained results with respect to some established competitors , while outperforming them when the exceptional objects are very rare .
We present SNIPER , an algorithm for performing efficient multi-scale training in instance level visual recognition tasks . Instead of processing every pixel in an image pyramid , SNIPER processes context regions around ground-truth instances ( referred to as chips ) at the appropriate scale .
This adds a new challenge to the DSE process . In this paper , using the multi-objective design space exploration tool FADSE , we tried to optimize the hardware and software parameters of the multicore SNIPER simulator running SPLASH-2 benchmarks suite . We optimized the hardware parameters ( nr cores , cache sizes , cache associativity , etc . ) and software parameters ( GCC optimizations , threads , and scheduler ) values that have been varied during the DSE process and shown the impact of these parameters on the optimization 's multi-objectives ( performance , area and power consumption ) .
The framework is developed for a population of n interconnected neurons . Several well-known neuron models are studied within the framework , including both excitatory and inhibitory simplified Hodgkin-Huxley , Hopf , and SNIPER models . For each model , we characterize the sets that the solutions to each system converge to .
We discuss digital forensics , digital steganography , and intrusion detection ( the use of learning techniques , the detection of masquerading , and honeypots for trapping intruders ) . Case studies include , for example : investigating Internet auction fraud with NetProbe ; graph mining for malware detection with Polonium ; link analysis with Coplink ; a project of the U.S. Federal Defense Financial Accounting Service ; information extraction tools for integration with a link analysis tool ; the Poznan ontology model for the link analysis of fuel fraud ; and fiscal fraud detection with the Pisa SNIPER project .
In this work , we consider a generic model for a saddle-node bifurcation on a limit cycle representative for neuron excitability type-I. It is given by N nonlocally coupled SNIPER oscillators in the oscillatory regime arranged on a ring . Depending on the system parameters we obtain chimera states with multiple coherent regions ( clustered chimeras ) , coexisting traveling waves , and we observe a flip in the mean phase velocities of the coherent and incoherent regions .
We use a generic model for type-I excitability ( known as the SNIPER or SNIC model ) to describe the local dynamics of nodes within a network in the presence of non-zero coupling delays . Utilising the method of the Master Stability Function , we investigate the stability of the zero-lag synchronised dynamics of the network nodes and its dependence on the two coupling parameters , namely the coupling strength and delay time .
The saccades in all these R - CNN variants are thus single - type and single - object , in that there is a single type of processing of crops , and each crop produces at most a single object . AutoFocus , which builds upon SNIPER that improved R - CNN training , adds a branch to Faster R - CNN to predict the regions that are likely to contain small objects . Then it applies Faster R - CNN again to each of those regions by cropping the original pixels .
document : SNIPER : Efficient Multi - Scale Training We present SNIPER , an algorithm for performing efficient multi - scale training in instance level visual recognition tasks .
This paper also states the applications of this technology along with its advantages and various disadvantages . It uses a multi-scale training method that can run at various sizes offering an excellent relation speed and accuracy . We have based our model on yolo v2 all the while bringing our own changes for shifting from object detection to Face identification .
Specifically , we train our basketball detection model based on the Region-based Fully Convolutional Networks ( R-FCN ) which uses the fully convolutional Residual Network ( ResNet ) as the backbone network . What 's more , we apply some new techniques including Online Hard Example Mining ( OHEM ) , Soft-NMS and multi-scale training strategy to achieve higher detection accuracy . In detail , the OHEM method can reduce the cost of fine-tuning during training by calculating the loss of the RoIs .
However , there can be no doubt that inspecting defects on steel surface is always a data-limited task due to difficult sample collection and expensive expert labeling . Unlike the previous works in which only labeled samples are treated using supervised classifiers , we propose a semi-supervised learning ( SSL ) defect classification approach based on multi-training of two different networks : a categorized generative adversarial network ( GAN ) and a residual network . This method uses the GAN to generate a large number of unlabeled samples .
Named Entity Recognition ( NER ) for Chinese corpus such as social media text and medical records is a grand chanllenge as the entity boundary is not easy to be accurately clarified . In this work , we describe and evaluate a character-level tagger for Chinese NER , which incorporates multitask learning , self-attention and multi-step training methods to exploit richer features and further improve the model performance . The proposed model has achieved 90.52 % strict F1 on the Electronic medical records dataset ( CCKS-NER 2017 ) , which is the best single model at present .
We propose a multi-step training method for designing generalized linear classifiers . First , an initial multi-class linear classifier is found through regression .
Our approach alleviates the paired training constraint by introducing a physical-model based disentanglement and reconstruction mechanism . A multi-scale adversarial training is employed to generate perceptually haze-free images . Experimental results on synthetic datasets demonstrate our superior performance compared with the state-ofthe-art methods in terms of PSNR , SSIM and CIEDE2000 .
In this paper , we present a novel depth map inpainting method for Kinect with a multi-scale deep Convolutional Neural Network ( CNN ) . This method has three stages : depth map pre-processing , multi-scale network training and image optimization . Our method provides a possibility to make a comprehensive refinement for Kinect depth map , including denoising , filling up small holes and inpainting large areas of depth missing .
In this network , reverse connected structure enriches the semantic context information of previous layers , while separable convolution is introduced for sparse representation of heavy feature maps generated from subnetworks . Further , we use multi-scale training scheme , online hard example mining , and model compression technique to accelerate the training process as well as reduce the parameters . Experimental results on Pascal Visual Object Classes ( VOC ) 2007 + 2012 and MicroSoft Common Objects in COntext ( MS COCO ) 2014 demonstrate the proposed method yields state-of-the-art performance .
Scale variation remains a challenge problem for object detection . Common paradigms usually adopt multi-scale training & testing ( image pyramid ) or FPN ( feature pyramid network ) to process objects in wide scale range . However , multi-scale methods aggravate more variation of scale that even deep convolution neural networks with FPN can not handle well .
Multi-times training is the tendency used in the middle-distance training all over the world , in the three years ' practice , we have made a satisfactory achiement .
The invention discloses an image quality testing method based on a deep convolutional neural network . The image quality testing method based on the deep convolutional neural network comprises the following steps of firstly , establishing a sample set , establishing a deep convolutional neural network model , training the deep convolutional neural network model under different initial conditions , connecting the optimal deep convolutional neural network models obtained through the multiple times of training in parallel to obtain an image quality testing system , and using the obtained image quality testing system for testing images to be tested . According to the image quality testing method based on the deep convolutional neural network , characteristic learning is carried out by simulating the learning process of human brains , the problem that characteristics are difficult to select in an existing image quality testing method is solved , the contingency of predicting results is reduced , integration is high , generalization ability is strong , and testing effects are good .
Our single - scale trained detector has already achieved a result of , which outperforms the R - FCN by 3.9 points . In addition , the multi - scale training further improves the performance up to . Interestingly , we observed that the more challenging the dataset , the more the promotion ( , for VOC07 , for VOC12 and for COCO , all in multi - scale training ) , which directly proves that our approach can effectively cope with a variety of complex situations .
Hence , the number of chips generated per image during training adaptively changes based on the scene complexity . SNIPER only processes 30 % more pixels compared to the commonly used single scale training at 800x1333 pixels on the COCO dataset . But , it also observes samples from extreme resolutions of the image pyramid , like 1400x2000 pixels .
For example , constructing a scale image pyramid ( e.g. scales= x , x , x ) requires processing times the number of pixels present in the original image . For this reason , it is impractical to use multi - scale training in many scenarios . Recently , it is shown that ignoring gradients of objects which are of extreme resolutions is beneficial while using multiple scales during training .
If we do not include regions in the image containing negatives which are similar in appearance to positive instances , it would increase our false positive rate and adversely affect detection performance . SNIPER is an efficient multi - scale training algorithm . In all experiments in this paper we use the aforementioned three scales ( See Section [ reference ] for the details ) .
Training with variable - size images increases scale - invariance and reduces over - fitting . We develop a simple multi - size training method . For a single network to accept variable input sizes , we approximate it by multiple networks that share all parameters , while each of these networks is trained using a fixed input size . In each epoch we train the network with a given input size , and switch to another input size for the next epoch . Experiments show that this multi - size training converges just as the traditional single - size training , and leads to better testing accuracy . The advantages of SPP are orthogonal to the specific CNN designs .
This result is similar to the 50 - bin pyramid above ( 34.98 / 14.14 ) , but considerably better than the no - SPP counterpart ( 35.99 / 14.76 ) . subsubsection : Multi - size Training Improves Accuracy Table [ reference ]
The VGG16 SPPnet results were computed by the authors of [ reference ] . SPPnet uses five scales during both training and testing . The improvement of Fast R - CNN over SPPnet illustrates that even though Fast R - CNN uses single - scale training and testing , fine - tuning the conv layers provides a large improvement in mAP ( from 63.1 % to 66.9 % ) . R - CNN achieves a mAP of 66.0 % .
Yet it achieves a mAP of 66.9 % , which is slightly higher than the 66.0 % reported for R - CNN [ reference ] , even though R - CNN uses " infinite " scales in the sense that each proposal is warped to a canonical size . Since single - scale processing offers the best tradeoff between speed and accuracy , especially for very deep models , all experiments outside of this sub - section use single - scale training and testing with s = 600 pixels . section : Do we need more training data ?
Second , we propose to use position - sensitive average pooling instead of normal average pooling for the last feature voting in R - FCN , which leads to an improved embedding . Third , multi - scale training strategy and on - line Hard Example Mining ( OHEM ) strategy are adopted for training . In the testing phase , we also ensemble the multi - scale detection results to improve the performance .
Unless noted , their default settings were used . End - to - end training was used instead of multi - step training . section : Faster - RCNN :
SNIP - SINDA-NASTRAN INTERFACING PROGRAM Status and quality of open access journals in Scopus Development of GNSS Receiver for Mobile CORS with RTK Correction Services Using Cloud Server An Automated Fault Tree Generation Approach With Fault Configuration Based on Model Checking Heat diffusion approach for scientific impact analysis in social media Performance Management within Social Network Sites: The Social Network Intelligence Process Method Model checking software product lines with SNIP New solution approaches for the maximum-reliability stochastic network interdiction problem The Respiratory System Robust Face Detection via Learning Small Faces on Hard Images Mining models of exceptional objects through rule learning SNIPER: Efficient Multi-Scale Training Multi-objective hardware-software co-optimization for the SNIPER multi-core simulator A framework for modeling and analysis of dynamical properties of spiking neurons Accounting for Social, Spatial, and Textual Interconnections Chimera States in Neuronal Systems of Excitability Type-I Synchronisation in networks of delay-coupled type-I excitable systems CornerNet-Lite: Efficient Keypoint Based Object Detection SNIPER: Efficient Multi-Scale Training EYE SPY Face Detection and Identification using YOLO Automatic Basketball Detection in Sport Video Based on R-FCN and Soft-NMS Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network Multitask Learning for Chinese Named Entity Recognition Multi-step Training of a Generalized Linear Classifier Towards Perceptual Image Dehazing by Physics-Based Disentanglement and Adversarial Training Kinect Depth Map Inpainting using a Multi-Scale Deep Convolutional Neural Network Separable reverse connected network for efficient multi-scale vehicle detection Consistent Scale Normalization for Object Recognition An analysis of middle-distance racers' multi-time training Image quality testing method based on deep convolutional neural network CoupleNet: Coupling Global Structure with Local Parts for Object Detection SNIPER: Efficient Multi-Scale Training Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Fast R-CNN Detecting Faces Using Region-based Fully Convolutional Networks Cascade R-CNN: Delving Into High Quality Object Detection
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2020 IEEE Winter Conference on Applications of Computer Vision (WACV) SAC ACC ECCS ArXiv NeurIPS CACRE PCM (2) AAAI 2017 IEEE International Conference on Computer Vision (ICCV) NeurIPS IEEE Transactions on Pattern Analysis and Machine Intelligence 2015 IEEE International Conference on Computer Vision (ICCV) ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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