Recently , discriminative training methods have been applied to feature extraction for speech recognition . In this paper , we apply the Minimum Classification Error estimation to train the parameters of a feature extractor . This feature extractor is a linear transformation of the original representation space . Because the previous circuit(s ) and the revised circuit are very similar , the model can be applied with small error . We proposed seven feature extractions , which are simple and scalable for large designs . Our experiment results show that prediction accuracy ( average error 3.7mV ) and correlation ( 0.55 ) are very high for a three million-gate real design . The resulting framework can be used as a first analysis toolbox to investigate the potential of the data . It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors . In this paper we explain the motivation of such a framework , we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox . On a single NVIDIA Titan X GPU , we measured an average speed of 35 and 40 frames per second , respectively for the two - branch and three - branch variants . Note that the highest computational burden comes from the feature extractor f θ . As shown in Figure [ reference ] , our method consists of three phases . First , we train a DNN on large - scale data , e.g. on miniImageNet ( - class , - shot ) , and then fix the low - level layers as Feature Extractor ( Section [ reference ] ) . Second , in the meta - transfer learning phase , MTL learns the Scaling and Shifting For instance , for miniImageNet , there are totally classes in the training split of and each class contains samples used to pre - train a - class classifier . We first randomly initialize a feature extractor ( e.g. CONV layers in ResNets ) and a classifier ( e.g. the last FC layer in ResNets ) , and then optimize them by gradient descent as follows , where denotes the following empirical loss , e.g. cross - entropy loss , and denotes the learning rate . In this phase , the feature extractor is learned . In the DNN - HMM experiments , the DNN is trained to predict context - dependent phone targets . The feature extraction is based on blocking the signal into frames of 25 ms with an overlap of 10 ms . The experimental activity is conducted considering different acoustic features , i.e. , 39 MFCCs ( 13 static + + ) , 40 log - mel filter - bank features ( FBANKS ) , as well as 40 fMLLR features ( extracted as reported in the s5 recipe of Kaldi ) . Feature engineering has been extensively studied by the machine learning community under various headings . In networks , the conventional paradigm for generating features for nodes is based on feature extraction techniques which typically involve some seed hand - crafted features based on network properties . In contrast , our goal is to automate the whole process by casting feature extraction as a representation learning problem in which case we do not require any hand - engineered features . A digital architecture of the asynchronous fuzzy RS flip-flop was given . A conception of the hardware implementation of the FPN was described . The ability of the proposed method to reduce FPN is measured by evaluating the structural similarity ( SSIM ) index employing a blackbody-based FPN reduction technique as a reference . This new interpretation of the FPN opens avenues to create new cameras for low-energy photons with the ability to perform denoising by digital refocusing . Based on both-branch fuzzy logic and fuzzy Petri net ( FPN ) , a new network attack model named both-branch fuzzy logic Petri-net attack net ( BBFPAN ) is put forward ; the promoting and suppressive factors to network attack are analyzed in this attack model . In order to make fine distinction between attack actions and effects , the process of attack and defense actions are depicted as places of FPN and system states are depicted as transitions . Two parameters are set : deterministic factor is used to express confidence degree of fuzzy rules while reliability value is used to express the reliability degree of storing proposition in decision information place . In addition , the implementation process for manufacture collaborative design is fuzzily deducted by FPN algorithm . According to the deduction result , the implementation flow of collaborative design process is decided . The fuzzy Petri net model was used to diagnose the transformer according to the optimal parameters . The simulation results show that the proposed algorithm has faster convergence speed and the higher correctness than the method of BP Optimized FPN ( BP-FPN ) and BA Optimized FPN network(BA-FPN ) , and has certain application valu e in the transformer fault diagnosis This article proposes a generalized definition of the fuzzy Petri net FPN and the reasoning structures of transitions in the FPN . Three types of fuzzy variables , local fuzzy variables , fuzzy marking variables , and global fuzzy variables , are used to model uncertainty based on different aspects of fuzzy information . In this paper , we propose a novel anchor-free region proposal network ( AF-RPN ) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem . Compared with the anchor-based region proposal generation approaches ( e.g. , RPN , FPN – RPN , RRPN and FPN – RRPN ) , AF-RPN can get rid of complicated anchor design and achieves higher recall rate on both horizontal and multi-oriented text detection benchmark tasks . Owing to the high-quality text proposals , our Faster R-CNN-based two-stage text detection approach achieves the state-of-the-art results on ICDAR-2017 MLT , COCO-Text , ICDAR-2015 and ICDAR-2013 text detection benchmark tasks by only using single-scale and single-model testing . In CMOS image sensors tiny semiconductor variations cause a distortion of the image known as fixed pattern noise ( FPN ) . With a good FPN compensation CMOS sensors can deliver very good image quality . Compensation algorithms differ in complexity , maximum frame rate , and compensation quality with thermal drift . The first one uses Mobilenet v1 FPN as feature extractor ( moSSD ) . The second one uses ResNet50 v1 FPN ( resSSD ) . Both models are pre-trained on Microsoft Common Objects in Context ( COCO ) dataset . Petri Nets are graphical and mathematical modeling tools for describing information processing systems whereas Fuzzy Set Theory provides a quantitative environment for representing subjective information . The framework used herein is a Fuzzy-Petri Net ( FPN ) , a synergy of Petri Nets and Fuzzy Set Theory . We demonstrate features of the FPN model used for managing a knowledge base available for the solution of a health risk based design problem of benzene contaminated groundwater . For the illuminated-scene detection requirements , an adaptive threshold algorithm is proposed . Based on the FPN detection results , performing a 3x3 window median grayscale substitution algorithm to correct them one by one . The experimental results show that the algorithm can detect the position coordinate information of FPN accurately , remove the influence of FPN effectively , and can be widely applied to sCMOS cameras with high requirements for image quality . ) 3D head pose , directly from image intensities . We further explain how this FacePoseNet ( FPN ) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks . We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments . Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet . Using a ResNet - FPN backbone for feature extraction with Mask R - CNN gives excellent gains in both accuracy and speed . For further details on FPN , we refer readers to [ reference ] . Faster R - CNN w / ResNet [ reference ] Faster R - CNN w / FPN [ reference ] Figure 4 . Head Architecture : We extend two existing Faster R - CNN heads [ reference ][ reference ] . Left / Right panels show the heads for the ResNet C4 and FPN backbones , from [ reference ] and [ reference ] , respectively , to which a mask branch is added . Numbers denote spatial resolution and channels . This includes MNC [ reference ] and FCIS [ reference ] , the winners of the COCO 2015 and 2016 segmentation challenges , respectively . Without bells and whistles , Mask R - CNN with ResNet - 101 - FPN backbone outperforms FCIS +++ [ reference ] , which includes multi - scale train / test , horizontal flip test , and online hard example mining ( OHEM ) [ reference ] . section : Timing Inference : We train a ResNet - 101 - FPN model that shares features between the RPN and Mask R - CNN stages , following the 4 - step training of Faster R - CNN [ reference ] . This model runs at 195ms per image on an Nvidia Tesla M40 GPU ( plus 15ms CPU time resizing the outputs to the original resolution ) , and achieves statistically the same mask AP as the unshared one . Mask R - CNN is also fast to train . Training with ResNet - 50 - FPN on COCO trainval35k takes 32 hours in our synchronized 8 - GPU implementation ( 0.72s per 16 - image mini - batch ) , and 44 hours with ResNet - 101 - FPN . In fact , fast prototyping can be completed in less than one day when training on the train set . We note that as in instance segmentation , the K keypoints are still treated independently . We adopt the ResNet - FPN variant , and the keypoint head architecture is similar to that in Figure 4 ( right ) . The keypoint head consists of a stack of eight 3×3 512 - d conv layers , followed by a deconv layer and 2× bilinear upscaling , producing an output resolution of 56×56 . The Faster R - CNN method further proposes the region proposal networks to generate bounding boxes with various scales . Based on the Faster R - CNN , the FPN approach introduces feature pyramid to extract features with different scales from a single image . The SSD method utilizes feature maps from different stages to process visual information at different scales . RoIAlign [ reference ] was used for a stronger baseline . This is denoted as FPN + and was used in all ablation studies . As usual , ResNet - 50 was used for ablation studies , and ResNet - 101 for final detection . DeblurGAN-V2 is based on a relativistic conditional GAN with a double-scale discriminator . For the first time , we introduce the Feature Pyramid Network into deblurring , as a core building block in the generator of DeblurGAN-V2 . It can flexibly work with a wide range of backbones , to navigate the balance between performance and efficiency . This paper proposes a defect detection system based on X-ray oriented deep learning , which focuses on approaches that improve the detection accuracy at both the algorithm and data augmentation levels . Feature Pyramid Network ( FPN ) was primarily adopted for algorithm modification , which proved to be better suited for detecting small defects than Faster R-CNN , with a 40.9 % improvement of the mean of Average Precision ( mAP ) value . In the final regression and classification stage , RoIAlign indicated apparent accuracy improvement in bounding boxes location compared with RoI pooling , which could increase accuracy by 23.6 % under Faster R-CNN . The detection network can efficiently generate the inclined rectangular box for ship . First , we propose the Feature Fusion Pyramid Network ( FFPN ) that strengthens the reuse of different scales features , and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships . Second , in order to get accurate ship angle information , we apply deep reinforcement learning to the inclined ship detection task for the first time . Our numerical results show that the two networks adapt well to the domain with Faster-RCNN exhibiting marginally better precision with more than 1 % difference . However , RetinaNet stands out with promising recall values indicating Feature Pyramid Network architecture can potentially detect technical documents better . In this paper , we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet , VGG and Resnet . Moreover , inspired by the feature pyramid network architecture , a hierarchical convolutional neural network ( CNN ) deep learning framework which is efficient in crack segmentation is also proposed , and the performance of it is compared with other state of the art network architecture . We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark-dataset on the internet for crack detection and segmentation , which is open-sourced for the research community . However , the approaches using global feature correlations fail to capture small , intricate textures and maintain correct texture scales of the artworks , and the approaches based on local patches are defective on global effect . In this paper , we present a novel feature pyramid fusion neural network , dubbed GLStyleNet , which sufficiently takes into consideration multi-scale and multi-level pyramid features by best aggregating layers across a VGG network , and performs style transfer hierarchically with multiple losses of different scales . Our proposed method retains high-frequency pixel information and low frequency construct information of images from two aspects : loss function constraint and feature fusion . First , the feature extraction layer is lightweighted by referring to YOLOv2 . Second , a new feature pyramid network layer is designed by improving its structure in YOLOv3 . Third , a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60 % anchors . In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection , a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed . Firstly , the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure ; at the same time , the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel . Then , the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps , and the scale-invariant convolution is performed again on these layers . Specifically , the size of a lesion instance is usually very small , compared with the original resolution of the fundus images , making them diffcult to be detected . We analyze the lesion-vs-image scale carefully and propose a large-size feature pyramid network ( LFPN ) to preserve more image details for mini lesion instance detection . Our method includes an effective region proposal strategy to increase the sensitivity . The architecture of our model is named New FPN ( New Feature Pyramid Network ) . Based on the structure of Feature Pyramid Network , we design a novel New Feature Pyramid Network , which consists of a combination of top-down and bottom-up connections to fuse features across scales , and achieves high-level semantic feature map at all scales . The experimental results show that New FPN achieves state-of-the-art detection accuracy ( i.e. 79.2%mAP ) on PASCAL VOC 2007 with high efficiency ( i.e. 73FPS ) . In this paper , based on FPN , we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection . The proposed method , is called a Weighted Feature Pyramid Network ( WFPN ) , and shows significant improvement over the traditional feature pyramids in several applications . Using WFPN in Faster R-CNN system , the proposed method achieves better performance on the PASCAL detection benchmark . We also explore another more effective backbone recently proposed by Lin et al . [ reference ] , called a Feature Pyramid Network ( FPN ) . FPN uses a top - down architecture with lateral connections to build an in - network feature pyramid from a single - scale input . Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet . We show that these are important for detecting small objects . The goal of this paper is to naturally leverage the pyramidal shape of a ConvNet 's feature hierarchy while creating a feature pyramid that has strong semantics at all scales . To achieve this goal , we rely on an architecture that combines low - resolution , semantically strong features with high - resolution , semantically weak features via a top - down pathway and lateral connections Detection speed : Due to the simple form of aggregate channel features and fast computation of feature pyramid , detection is quite efficient . For full yaw pose face detection in VGA image , the proposed detector using single - scale features runs at FPS on a single thread and FPS if threads are used . This is possible because the covariance matrix Σ is shared across all patches per channel and hence the derived Q is likewise spatially invariant . Decorrelating all 10 channels in an entire feature pyramid for a 640× 480 image takes about .5 seconds . Although more parameters and complex structures would improve the performance , the spirit of direct regression still has limitations in real - time applications , for example in face detection , the size of faces can vary from to . To handle the scale variation in a CNN - based detection system in terms of both efficiency and accuracy , we are inspired by the fast feature pyramid work proposed by Dollár et al . , where a detection system using hand - crafted features is designed for pedestrian detection . It is found that image gradients across scales can be predicted based on natural image statistics . The resulting base network architecture has about 66.7 % top - 1 classification accuracy on the CLS - LOC validation dataset and only needs about 1.2ms to process a image on a NVIDIA GTX 1080 . We then add the feature pyramid layers from conv3_3 , conv4_3 , conv5_3 , and fc7 , which are used to predict region proposals with scales of 16 , 32 , 64 , 128 respectively . We also add an extra convolutional layer ( conv8 ) which halves the fc7 feature map size , and use it to predict proposals with scale of 256 . [ reference ] . We adopt the highly modularized stacked Hourglass Network as the basic network structure to investigate feature pyramid learning for human pose estimation . The building block of our network is the proposed Pyramid Residual Module ( PRM ) . At the same time , they can also be trained jointly in an end - to - end manner . We adopt the settings introduced in , involving the construction of Feature Pyramid Network features , and ROI - Align pooling , which have been shown to be important for tasks that require spatial accuracy . We adapt this architecture to our task , so as to obtain dense part labels and coordinates within each of the selected regions . However , face detection remains a challenging task due to occlusion , illumination , makeup , as well as pose and scale variance , as shown in the benchmark dataset WIDER FACE . Current state - of - the - art CNN - based face detectors attempt to address these challenges by employing more powerful backbone models , exploiting feature pyramid - style architectures to combine features from multiple detection feature maps , designing denser anchors and utilizing larger contextual information . These methods and techniques have been shown to be successful to build a robust face detector , and improve the performance towards human - level for most images . For typical complex scene understanding , previously to get a global image - level feature , spatial pyramid pooling was widely employed where spatial statistics provide a good descriptor for overall scene interpretation . Spatial pyramid pooling network further enhances the ability . Different from these methods , to incorporate suitable global features , we propose pyramid scene parsing network ( PSPNet ) . Although these object detectors with feature pyramids achieve encouraging results , they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale , pyramidal architecture of the backbones which are originally designed for object classification task . Newly , in this work , we present Multi-Level Feature Pyramid Network ( MLFPN ) to construct more effective feature pyramids for detecting objects of different scales . First , we fuse multi-level features ( i.e. multiple layers ) extracted by backbone as the base feature . Second , we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects . Finally , we gather up the decoder layers with equivalent scales ( sizes ) to construct a feature pyramid for object detection , in which every feature map consists of the layers ( features ) from multiple levels . To evaluate the effectiveness of the proposed MLFPN , we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD , and achieve better detection performance than state-of-the-art one-stage detectors . Specifically , on MSCOCO benchmark , M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy , which are the new stateof-the-art results among one-stage detectors . This paper introduces a new method of empowering Logical Petri Nets ( LPN ) and Fuzzy Logical Petri Nets ( FLPN ) [ 1 ] using multiple logic . In this paper we proposed a Multi-Logical Petri Nets ( MLPN ) and Multi-Logical Fuzzy Petri Nets ( MLFPN ) based on Logical Petri Nets ( LPN ) and Fuzzy Logical Petri Nets ( FLPN ) . Here we have showed that , how multiple logic is implemented in MLPN and MLFPN .
Recently , discriminative training methods have been applied to feature extraction for speech recognition . In this paper , we apply the Minimum Classification Error estimation to train the parameters of a feature extractor . This feature extractor is a linear transformation of the original representation space .
Because the previous circuit(s ) and the revised circuit are very similar , the model can be applied with small error . We proposed seven feature extractions , which are simple and scalable for large designs . Our experiment results show that prediction accuracy ( average error 3.7mV ) and correlation ( 0.55 ) are very high for a three million-gate real design .
The resulting framework can be used as a first analysis toolbox to investigate the potential of the data . It can also be used to fine-tune parameters of machine learning algorithms or parameters of features extractors . In this paper we explain the motivation of such a framework , we present its structure and we show a case study presenting negative results that we could quickly spot using our toolbox .
On a single NVIDIA Titan X GPU , we measured an average speed of 35 and 40 frames per second , respectively for the two - branch and three - branch variants . Note that the highest computational burden comes from the feature extractor f θ .
As shown in Figure [ reference ] , our method consists of three phases . First , we train a DNN on large - scale data , e.g. on miniImageNet ( - class , - shot ) , and then fix the low - level layers as Feature Extractor ( Section [ reference ] ) . Second , in the meta - transfer learning phase , MTL learns the Scaling and Shifting
For instance , for miniImageNet , there are totally classes in the training split of and each class contains samples used to pre - train a - class classifier . We first randomly initialize a feature extractor ( e.g. CONV layers in ResNets ) and a classifier ( e.g. the last FC layer in ResNets ) , and then optimize them by gradient descent as follows , where denotes the following empirical loss , e.g. cross - entropy loss , and denotes the learning rate . In this phase , the feature extractor is learned .
In the DNN - HMM experiments , the DNN is trained to predict context - dependent phone targets . The feature extraction is based on blocking the signal into frames of 25 ms with an overlap of 10 ms . The experimental activity is conducted considering different acoustic features , i.e. , 39 MFCCs ( 13 static + + ) , 40 log - mel filter - bank features ( FBANKS ) , as well as 40 fMLLR features ( extracted as reported in the s5 recipe of Kaldi ) .
Feature engineering has been extensively studied by the machine learning community under various headings . In networks , the conventional paradigm for generating features for nodes is based on feature extraction techniques which typically involve some seed hand - crafted features based on network properties . In contrast , our goal is to automate the whole process by casting feature extraction as a representation learning problem in which case we do not require any hand - engineered features .
A digital architecture of the asynchronous fuzzy RS flip-flop was given . A conception of the hardware implementation of the FPN was described .
The ability of the proposed method to reduce FPN is measured by evaluating the structural similarity ( SSIM ) index employing a blackbody-based FPN reduction technique as a reference . This new interpretation of the FPN opens avenues to create new cameras for low-energy photons with the ability to perform denoising by digital refocusing .
Based on both-branch fuzzy logic and fuzzy Petri net ( FPN ) , a new network attack model named both-branch fuzzy logic Petri-net attack net ( BBFPAN ) is put forward ; the promoting and suppressive factors to network attack are analyzed in this attack model . In order to make fine distinction between attack actions and effects , the process of attack and defense actions are depicted as places of FPN and system states are depicted as transitions .
Two parameters are set : deterministic factor is used to express confidence degree of fuzzy rules while reliability value is used to express the reliability degree of storing proposition in decision information place . In addition , the implementation process for manufacture collaborative design is fuzzily deducted by FPN algorithm . According to the deduction result , the implementation flow of collaborative design process is decided .
The fuzzy Petri net model was used to diagnose the transformer according to the optimal parameters . The simulation results show that the proposed algorithm has faster convergence speed and the higher correctness than the method of BP Optimized FPN ( BP-FPN ) and BA Optimized FPN network(BA-FPN ) , and has certain application valu e in the transformer fault diagnosis
This article proposes a generalized definition of the fuzzy Petri net FPN and the reasoning structures of transitions in the FPN . Three types of fuzzy variables , local fuzzy variables , fuzzy marking variables , and global fuzzy variables , are used to model uncertainty based on different aspects of fuzzy information .
In this paper , we propose a novel anchor-free region proposal network ( AF-RPN ) to replace the original anchor-based RPN in the Faster R-CNN framework to address the above problem . Compared with the anchor-based region proposal generation approaches ( e.g. , RPN , FPN – RPN , RRPN and FPN – RRPN ) , AF-RPN can get rid of complicated anchor design and achieves higher recall rate on both horizontal and multi-oriented text detection benchmark tasks . Owing to the high-quality text proposals , our Faster R-CNN-based two-stage text detection approach achieves the state-of-the-art results on ICDAR-2017 MLT , COCO-Text , ICDAR-2015 and ICDAR-2013 text detection benchmark tasks by only using single-scale and single-model testing .
In CMOS image sensors tiny semiconductor variations cause a distortion of the image known as fixed pattern noise ( FPN ) . With a good FPN compensation CMOS sensors can deliver very good image quality . Compensation algorithms differ in complexity , maximum frame rate , and compensation quality with thermal drift .
The first one uses Mobilenet v1 FPN as feature extractor ( moSSD ) . The second one uses ResNet50 v1 FPN ( resSSD ) . Both models are pre-trained on Microsoft Common Objects in Context ( COCO ) dataset .
Petri Nets are graphical and mathematical modeling tools for describing information processing systems whereas Fuzzy Set Theory provides a quantitative environment for representing subjective information . The framework used herein is a Fuzzy-Petri Net ( FPN ) , a synergy of Petri Nets and Fuzzy Set Theory . We demonstrate features of the FPN model used for managing a knowledge base available for the solution of a health risk based design problem of benzene contaminated groundwater .
For the illuminated-scene detection requirements , an adaptive threshold algorithm is proposed . Based on the FPN detection results , performing a 3x3 window median grayscale substitution algorithm to correct them one by one . The experimental results show that the algorithm can detect the position coordinate information of FPN accurately , remove the influence of FPN effectively , and can be widely applied to sCMOS cameras with high requirements for image quality .
) 3D head pose , directly from image intensities . We further explain how this FacePoseNet ( FPN ) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks . We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments .
Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet . Using a ResNet - FPN backbone for feature extraction with Mask R - CNN gives excellent gains in both accuracy and speed . For further details on FPN , we refer readers to [ reference ] .
Faster R - CNN w / ResNet [ reference ] Faster R - CNN w / FPN [ reference ] Figure 4 . Head Architecture : We extend two existing Faster R - CNN heads [ reference ][ reference ] . Left / Right panels show the heads for the ResNet C4 and FPN backbones , from [ reference ] and [ reference ] , respectively , to which a mask branch is added . Numbers denote spatial resolution and channels .
This includes MNC [ reference ] and FCIS [ reference ] , the winners of the COCO 2015 and 2016 segmentation challenges , respectively . Without bells and whistles , Mask R - CNN with ResNet - 101 - FPN backbone outperforms FCIS +++ [ reference ] , which includes multi - scale train / test , horizontal flip test , and online hard example mining ( OHEM ) [ reference ] .
section : Timing Inference : We train a ResNet - 101 - FPN model that shares features between the RPN and Mask R - CNN stages , following the 4 - step training of Faster R - CNN [ reference ] . This model runs at 195ms per image on an Nvidia Tesla M40 GPU ( plus 15ms CPU time resizing the outputs to the original resolution ) , and achieves statistically the same mask AP as the unshared one .
Mask R - CNN is also fast to train . Training with ResNet - 50 - FPN on COCO trainval35k takes 32 hours in our synchronized 8 - GPU implementation ( 0.72s per 16 - image mini - batch ) , and 44 hours with ResNet - 101 - FPN . In fact , fast prototyping can be completed in less than one day when training on the train set .
We note that as in instance segmentation , the K keypoints are still treated independently . We adopt the ResNet - FPN variant , and the keypoint head architecture is similar to that in Figure 4 ( right ) . The keypoint head consists of a stack of eight 3×3 512 - d conv layers , followed by a deconv layer and 2× bilinear upscaling , producing an output resolution of 56×56 .
The Faster R - CNN method further proposes the region proposal networks to generate bounding boxes with various scales . Based on the Faster R - CNN , the FPN approach introduces feature pyramid to extract features with different scales from a single image . The SSD method utilizes feature maps from different stages to process visual information at different scales .
RoIAlign [ reference ] was used for a stronger baseline . This is denoted as FPN + and was used in all ablation studies . As usual , ResNet - 50 was used for ablation studies , and ResNet - 101 for final detection .
DeblurGAN-V2 is based on a relativistic conditional GAN with a double-scale discriminator . For the first time , we introduce the Feature Pyramid Network into deblurring , as a core building block in the generator of DeblurGAN-V2 . It can flexibly work with a wide range of backbones , to navigate the balance between performance and efficiency .
This paper proposes a defect detection system based on X-ray oriented deep learning , which focuses on approaches that improve the detection accuracy at both the algorithm and data augmentation levels . Feature Pyramid Network ( FPN ) was primarily adopted for algorithm modification , which proved to be better suited for detecting small defects than Faster R-CNN , with a 40.9 % improvement of the mean of Average Precision ( mAP ) value . In the final regression and classification stage , RoIAlign indicated apparent accuracy improvement in bounding boxes location compared with RoI pooling , which could increase accuracy by 23.6 % under Faster R-CNN .
The detection network can efficiently generate the inclined rectangular box for ship . First , we propose the Feature Fusion Pyramid Network ( FFPN ) that strengthens the reuse of different scales features , and FFPN can extract the low level location and high level semantic information that has an important impact on multi-scale ship detection and precise location of dense parking ships . Second , in order to get accurate ship angle information , we apply deep reinforcement learning to the inclined ship detection task for the first time .
Our numerical results show that the two networks adapt well to the domain with Faster-RCNN exhibiting marginally better precision with more than 1 % difference . However , RetinaNet stands out with promising recall values indicating Feature Pyramid Network architecture can potentially detect technical documents better .
In this paper , we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet , VGG and Resnet . Moreover , inspired by the feature pyramid network architecture , a hierarchical convolutional neural network ( CNN ) deep learning framework which is efficient in crack segmentation is also proposed , and the performance of it is compared with other state of the art network architecture . We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark-dataset on the internet for crack detection and segmentation , which is open-sourced for the research community .
However , the approaches using global feature correlations fail to capture small , intricate textures and maintain correct texture scales of the artworks , and the approaches based on local patches are defective on global effect . In this paper , we present a novel feature pyramid fusion neural network , dubbed GLStyleNet , which sufficiently takes into consideration multi-scale and multi-level pyramid features by best aggregating layers across a VGG network , and performs style transfer hierarchically with multiple losses of different scales . Our proposed method retains high-frequency pixel information and low frequency construct information of images from two aspects : loss function constraint and feature fusion .
First , the feature extraction layer is lightweighted by referring to YOLOv2 . Second , a new feature pyramid network layer is designed by improving its structure in YOLOv3 . Third , a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60 % anchors .
In order to improve the detection rate of the traditional single-shot multibox detection algorithm in small object detection , a feature-enhanced fusion SSD object detection algorithm based on the pyramid network is proposed . Firstly , the selected multiscale feature layer is merged with the scale-invariant convolutional layer through the feature pyramid network structure ; at the same time , the multiscale feature map is separately converted into the channel number using the scale-invariant convolution kernel . Then , the obtained two sets of pyramid-shaped feature layers are further feature fused to generate a set of enhanced multiscale feature maps , and the scale-invariant convolution is performed again on these layers .
Specifically , the size of a lesion instance is usually very small , compared with the original resolution of the fundus images , making them diffcult to be detected . We analyze the lesion-vs-image scale carefully and propose a large-size feature pyramid network ( LFPN ) to preserve more image details for mini lesion instance detection . Our method includes an effective region proposal strategy to increase the sensitivity .
The architecture of our model is named New FPN ( New Feature Pyramid Network ) . Based on the structure of Feature Pyramid Network , we design a novel New Feature Pyramid Network , which consists of a combination of top-down and bottom-up connections to fuse features across scales , and achieves high-level semantic feature map at all scales . The experimental results show that New FPN achieves state-of-the-art detection accuracy ( i.e. 79.2%mAP ) on PASCAL VOC 2007 with high efficiency ( i.e. 73FPS ) .
In this paper , based on FPN , we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection . The proposed method , is called a Weighted Feature Pyramid Network ( WFPN ) , and shows significant improvement over the traditional feature pyramids in several applications . Using WFPN in Faster R-CNN system , the proposed method achieves better performance on the PASCAL detection benchmark .
We also explore another more effective backbone recently proposed by Lin et al . [ reference ] , called a Feature Pyramid Network ( FPN ) . FPN uses a top - down architecture with lateral connections to build an in - network feature pyramid from a single - scale input . Faster R - CNN with an FPN backbone extracts RoI features from different levels of the feature pyramid according to their scale , but otherwise the rest of the approach is similar to vanilla ResNet .
We show that these are important for detecting small objects . The goal of this paper is to naturally leverage the pyramidal shape of a ConvNet 's feature hierarchy while creating a feature pyramid that has strong semantics at all scales . To achieve this goal , we rely on an architecture that combines low - resolution , semantically strong features with high - resolution , semantically weak features via a top - down pathway and lateral connections
Detection speed : Due to the simple form of aggregate channel features and fast computation of feature pyramid , detection is quite efficient . For full yaw pose face detection in VGA image , the proposed detector using single - scale features runs at FPS on a single thread and FPS if threads are used .
This is possible because the covariance matrix Σ is shared across all patches per channel and hence the derived Q is likewise spatially invariant . Decorrelating all 10 channels in an entire feature pyramid for a 640× 480 image takes about .5 seconds .
Although more parameters and complex structures would improve the performance , the spirit of direct regression still has limitations in real - time applications , for example in face detection , the size of faces can vary from to . To handle the scale variation in a CNN - based detection system in terms of both efficiency and accuracy , we are inspired by the fast feature pyramid work proposed by Dollár et al . , where a detection system using hand - crafted features is designed for pedestrian detection . It is found that image gradients across scales can be predicted based on natural image statistics .
The resulting base network architecture has about 66.7 % top - 1 classification accuracy on the CLS - LOC validation dataset and only needs about 1.2ms to process a image on a NVIDIA GTX 1080 . We then add the feature pyramid layers from conv3_3 , conv4_3 , conv5_3 , and fc7 , which are used to predict region proposals with scales of 16 , 32 , 64 , 128 respectively . We also add an extra convolutional layer ( conv8 ) which halves the fc7 feature map size , and use it to predict proposals with scale of 256 .
[ reference ] . We adopt the highly modularized stacked Hourglass Network as the basic network structure to investigate feature pyramid learning for human pose estimation . The building block of our network is the proposed Pyramid Residual Module ( PRM ) .
At the same time , they can also be trained jointly in an end - to - end manner . We adopt the settings introduced in , involving the construction of Feature Pyramid Network features , and ROI - Align pooling , which have been shown to be important for tasks that require spatial accuracy . We adapt this architecture to our task , so as to obtain dense part labels and coordinates within each of the selected regions .
However , face detection remains a challenging task due to occlusion , illumination , makeup , as well as pose and scale variance , as shown in the benchmark dataset WIDER FACE . Current state - of - the - art CNN - based face detectors attempt to address these challenges by employing more powerful backbone models , exploiting feature pyramid - style architectures to combine features from multiple detection feature maps , designing denser anchors and utilizing larger contextual information . These methods and techniques have been shown to be successful to build a robust face detector , and improve the performance towards human - level for most images .
For typical complex scene understanding , previously to get a global image - level feature , spatial pyramid pooling was widely employed where spatial statistics provide a good descriptor for overall scene interpretation . Spatial pyramid pooling network further enhances the ability . Different from these methods , to incorporate suitable global features , we propose pyramid scene parsing network ( PSPNet ) .
Although these object detectors with feature pyramids achieve encouraging results , they have some limitations due to that they only simply construct the feature pyramid according to the inherent multiscale , pyramidal architecture of the backbones which are originally designed for object classification task . Newly , in this work , we present Multi-Level Feature Pyramid Network ( MLFPN ) to construct more effective feature pyramids for detecting objects of different scales . First , we fuse multi-level features ( i.e. multiple layers ) extracted by backbone as the base feature . Second , we feed the base feature into a block of alternating joint Thinned U-shape Modules and Feature Fusion Modules and exploit the decoder layers of each Ushape module as the features for detecting objects . Finally , we gather up the decoder layers with equivalent scales ( sizes ) to construct a feature pyramid for object detection , in which every feature map consists of the layers ( features ) from multiple levels . To evaluate the effectiveness of the proposed MLFPN , we design and train a powerful end-to-end one-stage object detector we call M2Det by integrating it into the architecture of SSD , and achieve better detection performance than state-of-the-art one-stage detectors . Specifically , on MSCOCO benchmark , M2Det achieves AP of 41.0 at speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with multi-scale inference strategy , which are the new stateof-the-art results among one-stage detectors .
This paper introduces a new method of empowering Logical Petri Nets ( LPN ) and Fuzzy Logical Petri Nets ( FLPN ) [ 1 ] using multiple logic . In this paper we proposed a Multi-Logical Petri Nets ( MLPN ) and Multi-Logical Fuzzy Petri Nets ( MLFPN ) based on Logical Petri Nets ( LPN ) and Fuzzy Logical Petri Nets ( FLPN ) . Here we have showed that , how multiple logic is implemented in MLPN and MLFPN .
Minimum classification error transformations for improving speech recognition systems IR drop prediction of ECO-revised circuits using machine learning A Python Framework for Exhaustive Machine Learning Algorithms and Features Evaluations Fast Online Object Tracking and Segmentation: A Unifying Approach Meta-Transfer Learning for Few-Shot Learning Light Gated Recurrent Units for Speech Recognition node2vec: Scalable Feature Learning for Networks Digital Implementation of Fuzzy Petri Net Based on Asynchronous Fuzzy RS Flip-Flop Infrared light field imaging system free of fixed-pattern noise An Attack Model Based on Both-Branch Fuzzy Logic and Fuzzy Petri Net Decision-making and controlling for products collaborative design implement process based on fuzzy Petri net Fault Diagnosis of Transformer Based on Chaotic Bats Algorithm Optimizing Fuzzy Petri Net Representation and Analysis of Uncertainty Using Fuzzy Petri Nets An anchor-free region proposal network for Faster R-CNN-based text detection approaches Fixed pattern noise column drift compensation (CDC) for digital moving picture cameras OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes A fuzzy-petri net formalization of expert information for groundwater risk management Adaptive Detection and Correction of Fixed Pattern Noise in sCMOS Cameras FacePoseNet: Making a Case for Landmark-Free Face Alignment Mask R-CNN Res2Net: A New Multi-scale Backbone Architecture Cascade R-CNN: Delving Into High Quality Object Detection DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning A Ship Rotation Detection Model in Remote Sensing Images Based on Feature Fusion Pyramid Network and Deep Reinforcement Learning Deep Learning for the Detection of Tabular Information from Electronic Component Datasheets Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network Small Object Detection Algorithm Based on Feature Pyramid-Enhanced Fusion SSD Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features A New Feature Pyramid Network for Object Detection Weighted Feature Pyramid Networks for Object Detection Mask R-CNN Feature Pyramid Networks for Object Detection Aggregate channel features for multi-view face detection Local Decorrelation For Improved Pedestrian Detection Recurrent Scale Approximation for Object Detection in CNN Frustum PointNets for 3D Object Detection from RGB-D Data Learning Feature Pyramids for Human Pose Estimation DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Robust Face Detection via Learning Small Faces on Hard Images Pyramid Scene Parsing Network M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network MULTI-LOGICAL FUZZY PETRI NETS
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EUSIPCO VTS AINA 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Emerging Topics in Computational Intelligence KDD ICAISC IAS ICIP 2017 IEEE International Conference on Computer Vision Workshops (ICCVW) IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE transactions on pattern analysis and machine intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ISCC ROBIO ISPA/BDCloud/SocialCom/SustainCom IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE International Joint Conference on Biometrics NIPS 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI
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