But the depth also brings extremely high computational cost . In this paper , inspired by Inception module , we introduce a new convolutional neural network module that combines residual structure and multiple convolution . A residual structure is mainly adopted to solve the gradient vanishing problem . After character candidates are detected , we classify them into 709 Korean characters by using a classification network ( CLSN ) . Similar to the RPN , our CLSN consists of residual blocks with the Inception architecture . In experiments , we achieved 97.69 % of accuracy at 5.9fps on both detection and recognition of Korean characters in traffic guide signs . In Chapter 4 , I made use of object-oriented analysis to reveal some of the many objects that you will be working with to achieve a successful solution for the Outerlite micro site . If you recall , the chapter concluded without commencing the second phase of the inception process , known as object-oriented design . While you were able to properly analyze your system without any knowledge of design patterns , lacking such information is not practical when determining a proper design . Unlike conventional data augmentation strategy , GAN based augmentation approach generates data that correlates to original data distribution . In the case of Inception based model , it uses multiple size kernels with factorized convolution for hierarchical feature extraction . This helps to a significant reduction in trainable parameters and the problem of overfitting . To this end , a preliminary prototype was launched at eight different sites to test its viability . The inception design was constantly monitored and subsequently redesigned . In this manner , the design activity becomes the pivot of the study . In this paper , we propose an image super-resolution method ( SR ) using a light inception layer in convolutional network ( LICN ) . Due to the strong representation ability of our well-designed inception layer that can learn richer representation with less parameters , we can build our model with shallow architecture that can reduce the effect of vanishing gradients problem and save computational costs . Our model strike a balance between computational speed and the quality of the result . All electronic communications between team members were recorded and analyzed using McGrath 's time , interaction , and performance TIP framework and all teams completed pre and post surveys measuring their levels of cohesion , perceptions of the process and satisfaction with outcomes . It was found that teams that received training spent more time in the member support function , more time in the inception mode , and less time in the conflict resolution mode . In addition , teams receiving training had higher ending levels of cohesion , perception of the process and satisfaction with outcomes . Agile methodology is allow to makes changes to fit the trends , split the tasks into different phases , and emphasized interactions between customers , testers , and developer . Agile process flow will separate into concept phase , inception phase , iterations phase , release phase , production phase , and retirement phase . In conclusion , the proposed application could let user effectively share and trade their excessive foods with others without use of money . In order to solve this problem , we propose a simplified inception unit based convolutional neural network to reduce the compression distortion in HEVC . In the proposed method , a simplified inception unit is designed to analyze and extract image features . By stacking multiple inception units , the internal and border information of each frame can be fully extracted and the images are able to achieve higher quality . In order to increase thereceptive field of the neural network without adding a pooling layer , this paper replaces the large-scale symmetricconvolution with asymmetric convolution . In this paper , a multi-feature residual learning module combines theInception module with residual learning , so that the network can extract richer image information and the networkis closer to the sparse connection . Experiments show that the model can achieve good visual effects in thedenoising experiment of real CT images . These equalities of expectations are used to describe distributions by moments or cumulants , where are polynomials of the data . We replacing by the coding layer of an Inception model in order to obtain vision - relevant features and consider polynomials of the coding unit functions . For practical reasons we only consider the first two polynomials , that is , the first two moments : mean and covariance . DeepFace uses a deep network coupled with 3D alignment , to normalize facial pose by warping facial landmarks to a canonical position prior to encoding . DeepID2 + and DeepID3 extended the inception architecture to include joint Bayesian metric learning and multi - task learning for both identification and verification . These top performing convolutional network architectures have interesting common properties . Summary statistics of such per - frame features are then used for facial expression recognition . In , authors propose modification of Inception architecture to capture action unit activation which can be beneficial for facial expression recognition . Other works use various techniques to capture the temporal evolution of the per - features . On the one hand we have our traditional Zeiler & Fergus based architecture with convolutions ( see Table [ reference ] ) . On the other hand we have Inception based models that dramatically reduce the model size . Overall , in the final performance the top models of both architectures perform comparably . This makes it much harder to adapt it to new use - cases while maintaining its efficiency . For example , if it is deemed necessary to increase the capacity of some Inception - style model , the simple transformation of just doubling the number of all filter bank sizes will lead to a 4x increase in both computational cost and number of parameters . This might prove prohibitive or unreasonable in a lot of practical scenarios , especially if the associated gains are modest . We tested our overall network design , swapping in different layer modules based off of these insights . We experienced an increase in network performance after switching from standard convolutional layers with large filters and no reduction steps to newer methods like the residual learning modules presented by He et al . and “ Inception” - based designs . After the initial performance improvement with these types of designs , various additional explorations and modifications to the layers did little to further boost performance or training time . Unlike the previous methods , the framework establish direct mapping relationship between input CT and calcification , consequently , it can directly yield accurate results based on the given CT scans in testing process . In this framework , the intra-slice calcification features are collected by an U-DenseNet module , which is the combination of Dense Convolutional Network ( DenseNet ) and U-Net . Subsequently , 3D U-Net is performed to extract the inter-slice calcification feature . DenseNets have been shown to be a competitive model among recent convolutional network architectures . These networks utilize Dense Blocks , which are groups of densely connected layers where the output of a hidden layer is fed in as the input of every other layer following it . Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest . These tasks are tackled in this work , by suggesting two approaches : A ) First , DenseNets are applied to weekly labeled data to infer the attention map of the dataset ( i.e. Salience and CAM ) . We push further this idea by directing attention maps to the YOLO v2 Deepnet-based , detection framework to localize bird vocalizations . Overall , our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery . Our results also provide baseline accuracy against which the newly proposed models should be evaluated and suggest the DenseNet-based models are the first candidate for this task . Then , to model the interdependencies between the features of different convolutional layers , we propose the double squeeze-and-excitation module ( DSEM ) and construct the inter-layer feature reweight DenseNet ( ILFR-DenseNet ) . In the last step , we designed the MFR-DenseNet by combining the CFR-DenseNet and the ILFR-DenseNet with an ensemble learning approach . Our experiments demonstrate the effectiveness of CFR-DenseNet , ILFR-DenseNet , and MFR-DenseNet . Our automatic segmentation result shows high agreement ( DSC 87.14 % ) to the clinical segmentation results by experienced radiation oncologists . In addition , comparison with state-of-the-art methods shows that our ED-DenseNet model is superior in segmentation performance . It can choose the filter weights automatically and restore the standard convolution filter with a high sparsity one called S-Conv . Finally , we combine S-Conv with dense connections and formed a more effective network architecture , that is S-DenseNet . Experiments show that in Cifar10 , Cifar100 and SVHN datasets , we use only 40 % parameters and 20 % FLOPs of DenseNet , and the accuracy is improved by 1 % . In this paper , a large amount of singing clips and corresponding human gradings were collected . And an elaborate designed Bi-DenseNet was trained to discriminate the good singings from the poor ones . The experiments demonstrated the proposed network performs better than the existing networks for singing evaluation task . In order to address this issue , this paper proposes a novel and efficient end-to-end 3D face alignment framework . We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional , named MobDenseNet . Simultaneously , different loss functions are used to constrain 3D parameters based on 3D Morphable Model ( 3DMM ) and 3D vertices . During the feature extraction procedure , short connections allow different combinations of convolutional operators , resulting in a large number of equivalent feature scales . Similarly , densely connected layers in the DenseNet enable the network to process objects in a very wide range of scales . DPN combine the ResNet with DenseNet to enables feature re - usage ability of ResNet and the feature exploration ablity of DenseNet . [ reference ] for an illustration . The dense connectivity is similar to DenseNet but generalizing the design philosophy from feature to semantic level . It encourages information flow as well as feature reuse “ globally ” across the stacked U - Nets , yielding improved localization accuracy . It is therefore sensitive to the absolute location . Recent architectures such as ResNet and DenseNet employ average pooling , which is permutation invariant and hence offers a more global translation invariance . paragraph : Image retrieval section : CNN Architectures We experiment with VGGNet [ reference ] , GoogLeNet [ reference ] , ResNets [ reference ] , and DenseNets [ reference ] as base architectures for the Siamese network trained with PC to demonstrate that our method is insensitive to the choice of source architecture . section : Experimental Details Applying a second convolution of the same size will yield active sites , and so on . This rapid growth of the number of active sites is a poor prospect when implementing modern convolutional network architectures that comprise tens or even hundreds of convolutions , such as VGG networks , ResNets , and DenseNets . Of course , convolutional networks are not often applied to inputs that only have a single active site , but the aforementioned ‘ ‘ dilation ’ ’ problems are equally problematic when the input data comprises one - dimensional curves in spaces with two or more dimensions , or two - dimensional surfaces in three or more dimensions . and Top - 5 validation error by approximately and , respectively , over ResNet - by doubling feature channels in each layer . DenseNets densenet substantially improve performance by building residual blocks that concatenate rather than add feature channels . Table [ reference ] demonstrates that FractalNet resists performance degradation as we increase to obtain extremely deep networks ( layers for ) . The registered photos can be used to stitch textures or refine the geometry of 3D models . This method can be used independently or collaboratively in image-based modeling systems to effectively treat restrictive camera-model position configurations . The experiment shows that our method is more flexible than the methods using only geometrical or local feature information . There are uncountable environmental variables in real world and most application developers want to define as them . For such a reason , this paper first presents an image modeling system , and then applies the proposed system development for robust descriptor of environmental change . The system includes three core algorithms which are luminous source modeling development , illumination calibration development based on brightness distribution in reference images and geometrical modeling development . The authors present a simulation study of the effects of room reverberation on the performance of the maximum likelihood ( ML ) estimator of the time delay , which is obtained by maximizing the output of a generalized cross-correlator . To this end , synthetic microphone signals are generated with the image model technique and are used to evaluate the bias , variance and probability of anomaly of the ML estimator as a function of the room reflection coefficients and other external parameters of interest . The results clearly demonstrate the adverse effects of room reverberation on MLTDE performance . The decoder autoregressively generates an output image of pixel intensities , one channel per pixel at each time step . While doing so , it consumes the previously generated pixels and the input image representation generated by the encoder . For both the encoder and decoder , the Image Transformer uses stacks of self - attention and position - wise feed - forward layers , similar to aiayn . Still , the degree of similarity is different in the different cases , with classification requiring more invariance to appearance variations and copy detection sensitivity to small image details . In order to learn an image representation that satisfies the different trade - offs , we start from an existing image classification network . We use a generalized mean layer that converts a spatial activation map to a fixed - size vector . In this paper , based on a novel self-assembly platform consisting of self-propulsive centimetre-sized modules capable of aggregation on the surface of water , we study the effect of stochasticity and morphology ( shape ) on the yield of targeted formations in self-assembly processes . Specifically , we focus on a unique phenomenon : that a number of modules instantly compose a target product without forming intermediate subassemblies , some of which constitute undesired geometrical formations ( termed one-shot aggregation ) . Together with a focus on the role that the morphology of the modules plays , we validate the effect of one-shot aggregation with a kinetic rate mathematical model . We find the linearly increasing input channel by dense connection leads to heavy memory access cost , which causes computation overhead and more energy consumption . To solve the inefficiency of DenseNet , we propose an energy and computation efficient architecture called VoVNet comprised of One-Shot Aggregation ( OSA ) . The OSA not only adopts the strength of DenseNet that represents diversified features with multi receptive fields but also overcomes the inefficiency of dense connection by aggregating all features only once in the last feature maps . In this paper , we propose a new strategy for near-duplicate video retrieval that is based on shot aggregation . We investigate different methods for shot aggregation with the main objective to solve the difficult trade-off between performance , scalability and speed .
But the depth also brings extremely high computational cost . In this paper , inspired by Inception module , we introduce a new convolutional neural network module that combines residual structure and multiple convolution . A residual structure is mainly adopted to solve the gradient vanishing problem .
After character candidates are detected , we classify them into 709 Korean characters by using a classification network ( CLSN ) . Similar to the RPN , our CLSN consists of residual blocks with the Inception architecture . In experiments , we achieved 97.69 % of accuracy at 5.9fps on both detection and recognition of Korean characters in traffic guide signs .
In Chapter 4 , I made use of object-oriented analysis to reveal some of the many objects that you will be working with to achieve a successful solution for the Outerlite micro site . If you recall , the chapter concluded without commencing the second phase of the inception process , known as object-oriented design . While you were able to properly analyze your system without any knowledge of design patterns , lacking such information is not practical when determining a proper design .
Unlike conventional data augmentation strategy , GAN based augmentation approach generates data that correlates to original data distribution . In the case of Inception based model , it uses multiple size kernels with factorized convolution for hierarchical feature extraction . This helps to a significant reduction in trainable parameters and the problem of overfitting .
To this end , a preliminary prototype was launched at eight different sites to test its viability . The inception design was constantly monitored and subsequently redesigned . In this manner , the design activity becomes the pivot of the study .
In this paper , we propose an image super-resolution method ( SR ) using a light inception layer in convolutional network ( LICN ) . Due to the strong representation ability of our well-designed inception layer that can learn richer representation with less parameters , we can build our model with shallow architecture that can reduce the effect of vanishing gradients problem and save computational costs . Our model strike a balance between computational speed and the quality of the result .
All electronic communications between team members were recorded and analyzed using McGrath 's time , interaction , and performance TIP framework and all teams completed pre and post surveys measuring their levels of cohesion , perceptions of the process and satisfaction with outcomes . It was found that teams that received training spent more time in the member support function , more time in the inception mode , and less time in the conflict resolution mode . In addition , teams receiving training had higher ending levels of cohesion , perception of the process and satisfaction with outcomes .
Agile methodology is allow to makes changes to fit the trends , split the tasks into different phases , and emphasized interactions between customers , testers , and developer . Agile process flow will separate into concept phase , inception phase , iterations phase , release phase , production phase , and retirement phase . In conclusion , the proposed application could let user effectively share and trade their excessive foods with others without use of money .
In order to solve this problem , we propose a simplified inception unit based convolutional neural network to reduce the compression distortion in HEVC . In the proposed method , a simplified inception unit is designed to analyze and extract image features . By stacking multiple inception units , the internal and border information of each frame can be fully extracted and the images are able to achieve higher quality .
In order to increase thereceptive field of the neural network without adding a pooling layer , this paper replaces the large-scale symmetricconvolution with asymmetric convolution . In this paper , a multi-feature residual learning module combines theInception module with residual learning , so that the network can extract richer image information and the networkis closer to the sparse connection . Experiments show that the model can achieve good visual effects in thedenoising experiment of real CT images .
These equalities of expectations are used to describe distributions by moments or cumulants , where are polynomials of the data . We replacing by the coding layer of an Inception model in order to obtain vision - relevant features and consider polynomials of the coding unit functions . For practical reasons we only consider the first two polynomials , that is , the first two moments : mean and covariance .
DeepFace uses a deep network coupled with 3D alignment , to normalize facial pose by warping facial landmarks to a canonical position prior to encoding . DeepID2 + and DeepID3 extended the inception architecture to include joint Bayesian metric learning and multi - task learning for both identification and verification . These top performing convolutional network architectures have interesting common properties .
Summary statistics of such per - frame features are then used for facial expression recognition . In , authors propose modification of Inception architecture to capture action unit activation which can be beneficial for facial expression recognition . Other works use various techniques to capture the temporal evolution of the per - features .
On the one hand we have our traditional Zeiler & Fergus based architecture with convolutions ( see Table [ reference ] ) . On the other hand we have Inception based models that dramatically reduce the model size . Overall , in the final performance the top models of both architectures perform comparably .
This makes it much harder to adapt it to new use - cases while maintaining its efficiency . For example , if it is deemed necessary to increase the capacity of some Inception - style model , the simple transformation of just doubling the number of all filter bank sizes will lead to a 4x increase in both computational cost and number of parameters . This might prove prohibitive or unreasonable in a lot of practical scenarios , especially if the associated gains are modest .
We tested our overall network design , swapping in different layer modules based off of these insights . We experienced an increase in network performance after switching from standard convolutional layers with large filters and no reduction steps to newer methods like the residual learning modules presented by He et al . and “ Inception” - based designs . After the initial performance improvement with these types of designs , various additional explorations and modifications to the layers did little to further boost performance or training time .
Unlike the previous methods , the framework establish direct mapping relationship between input CT and calcification , consequently , it can directly yield accurate results based on the given CT scans in testing process . In this framework , the intra-slice calcification features are collected by an U-DenseNet module , which is the combination of Dense Convolutional Network ( DenseNet ) and U-Net . Subsequently , 3D U-Net is performed to extract the inter-slice calcification feature .
DenseNets have been shown to be a competitive model among recent convolutional network architectures . These networks utilize Dense Blocks , which are groups of densely connected layers where the output of a hidden layer is fed in as the input of every other layer following it .
Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest . These tasks are tackled in this work , by suggesting two approaches : A ) First , DenseNets are applied to weekly labeled data to infer the attention map of the dataset ( i.e. Salience and CAM ) . We push further this idea by directing attention maps to the YOLO v2 Deepnet-based , detection framework to localize bird vocalizations .
Overall , our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery . Our results also provide baseline accuracy against which the newly proposed models should be evaluated and suggest the DenseNet-based models are the first candidate for this task .
Then , to model the interdependencies between the features of different convolutional layers , we propose the double squeeze-and-excitation module ( DSEM ) and construct the inter-layer feature reweight DenseNet ( ILFR-DenseNet ) . In the last step , we designed the MFR-DenseNet by combining the CFR-DenseNet and the ILFR-DenseNet with an ensemble learning approach . Our experiments demonstrate the effectiveness of CFR-DenseNet , ILFR-DenseNet , and MFR-DenseNet .
Our automatic segmentation result shows high agreement ( DSC 87.14 % ) to the clinical segmentation results by experienced radiation oncologists . In addition , comparison with state-of-the-art methods shows that our ED-DenseNet model is superior in segmentation performance .
It can choose the filter weights automatically and restore the standard convolution filter with a high sparsity one called S-Conv . Finally , we combine S-Conv with dense connections and formed a more effective network architecture , that is S-DenseNet . Experiments show that in Cifar10 , Cifar100 and SVHN datasets , we use only 40 % parameters and 20 % FLOPs of DenseNet , and the accuracy is improved by 1 % .
In this paper , a large amount of singing clips and corresponding human gradings were collected . And an elaborate designed Bi-DenseNet was trained to discriminate the good singings from the poor ones . The experiments demonstrated the proposed network performs better than the existing networks for singing evaluation task .
In order to address this issue , this paper proposes a novel and efficient end-to-end 3D face alignment framework . We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional , named MobDenseNet . Simultaneously , different loss functions are used to constrain 3D parameters based on 3D Morphable Model ( 3DMM ) and 3D vertices .
During the feature extraction procedure , short connections allow different combinations of convolutional operators , resulting in a large number of equivalent feature scales . Similarly , densely connected layers in the DenseNet enable the network to process objects in a very wide range of scales . DPN combine the ResNet with DenseNet to enables feature re - usage ability of ResNet and the feature exploration ablity of DenseNet .
[ reference ] for an illustration . The dense connectivity is similar to DenseNet but generalizing the design philosophy from feature to semantic level . It encourages information flow as well as feature reuse “ globally ” across the stacked U - Nets , yielding improved localization accuracy .
It is therefore sensitive to the absolute location . Recent architectures such as ResNet and DenseNet employ average pooling , which is permutation invariant and hence offers a more global translation invariance . paragraph : Image retrieval
section : CNN Architectures We experiment with VGGNet [ reference ] , GoogLeNet [ reference ] , ResNets [ reference ] , and DenseNets [ reference ] as base architectures for the Siamese network trained with PC to demonstrate that our method is insensitive to the choice of source architecture . section : Experimental Details
Applying a second convolution of the same size will yield active sites , and so on . This rapid growth of the number of active sites is a poor prospect when implementing modern convolutional network architectures that comprise tens or even hundreds of convolutions , such as VGG networks , ResNets , and DenseNets . Of course , convolutional networks are not often applied to inputs that only have a single active site , but the aforementioned ‘ ‘ dilation ’ ’ problems are equally problematic when the input data comprises one - dimensional curves in spaces with two or more dimensions , or two - dimensional surfaces in three or more dimensions .
and Top - 5 validation error by approximately and , respectively , over ResNet - by doubling feature channels in each layer . DenseNets densenet substantially improve performance by building residual blocks that concatenate rather than add feature channels . Table [ reference ] demonstrates that FractalNet resists performance degradation as we increase to obtain extremely deep networks ( layers for ) .
The registered photos can be used to stitch textures or refine the geometry of 3D models . This method can be used independently or collaboratively in image-based modeling systems to effectively treat restrictive camera-model position configurations . The experiment shows that our method is more flexible than the methods using only geometrical or local feature information .
There are uncountable environmental variables in real world and most application developers want to define as them . For such a reason , this paper first presents an image modeling system , and then applies the proposed system development for robust descriptor of environmental change . The system includes three core algorithms which are luminous source modeling development , illumination calibration development based on brightness distribution in reference images and geometrical modeling development .
The authors present a simulation study of the effects of room reverberation on the performance of the maximum likelihood ( ML ) estimator of the time delay , which is obtained by maximizing the output of a generalized cross-correlator . To this end , synthetic microphone signals are generated with the image model technique and are used to evaluate the bias , variance and probability of anomaly of the ML estimator as a function of the room reflection coefficients and other external parameters of interest . The results clearly demonstrate the adverse effects of room reverberation on MLTDE performance .
The decoder autoregressively generates an output image of pixel intensities , one channel per pixel at each time step . While doing so , it consumes the previously generated pixels and the input image representation generated by the encoder . For both the encoder and decoder , the Image Transformer uses stacks of self - attention and position - wise feed - forward layers , similar to aiayn .
Still , the degree of similarity is different in the different cases , with classification requiring more invariance to appearance variations and copy detection sensitivity to small image details . In order to learn an image representation that satisfies the different trade - offs , we start from an existing image classification network . We use a generalized mean layer that converts a spatial activation map to a fixed - size vector .
In this paper , based on a novel self-assembly platform consisting of self-propulsive centimetre-sized modules capable of aggregation on the surface of water , we study the effect of stochasticity and morphology ( shape ) on the yield of targeted formations in self-assembly processes . Specifically , we focus on a unique phenomenon : that a number of modules instantly compose a target product without forming intermediate subassemblies , some of which constitute undesired geometrical formations ( termed one-shot aggregation ) . Together with a focus on the role that the morphology of the modules plays , we validate the effect of one-shot aggregation with a kinetic rate mathematical model .
We find the linearly increasing input channel by dense connection leads to heavy memory access cost , which causes computation overhead and more energy consumption . To solve the inefficiency of DenseNet , we propose an energy and computation efficient architecture called VoVNet comprised of One-Shot Aggregation ( OSA ) . The OSA not only adopts the strength of DenseNet that represents diversified features with multi receptive fields but also overcomes the inefficiency of dense connection by aggregating all features only once in the last feature maps .
In this paper , we propose a new strategy for near-duplicate video retrieval that is based on shot aggregation . We investigate different methods for shot aggregation with the main objective to solve the difficult trade-off between performance , scalability and speed .
Fast and High-performance Multi-convolution Deep Neural Network Structure with Residuals Fast Korean Text Detection and Recognition in Traffic Guide Signs Object-Oriented Design… A Revisit Performance improvement of mediastinal lymph node severity detection using GAN and Inception network The mobile phone as a link to formal financial services: findings from Uganda Super-resolution using a light inception layer in convolutional neural network The Impact of Training on Virtual Project Teams: A TIP Investigation Online sharing and exchange food service Simplified Inception Unit based Filter for HEVC Application of Artificial Intelligence-Based Digital Image Processing Technology in Medical Image Analysis GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Template adaptation for face verification and identification Covariance Pooling for Facial Expression Recognition FaceNet: A unified embedding for face recognition and clustering Rethinking the Inception Architecture for Computer Vision Stacked Hourglass Networks for Human Pose Estimation An end-to-end joint learning framework of artery-specific coronary calcium scoring in non-contrast cardiac CT ParaNet - Using Dense Blocks for Early Inference Deep Networks tag the location of bird vocalisations on audio spectrograms Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models Multiple Feature Reweight DenseNet for Image Classification Prostate Segmentation with Encoder-Decoder Densely Connected Convolutional Network (Ed-Densenet) S-DenseNet: A DenseNet Compression Model Based on Convolution Grouping Strategy Using Skyline Method Automatic Singing Evaluation without Reference Melody Using Bi-dense Neural Network Robust 3D Face Alignment with Efficient Fully Convolutional Neural Networks Res2Net: A New Multi-scale Backbone Architecture Quantized Densely Connected U-Nets for Efficient Landmark Localization MultiGrain: a unified image embedding for classes and instances Pairwise Confusion for Fine-Grained Visual Classification Submanifold Sparse Convolutional Networks FractalNet: Ultra-Deep Neural Networks without Residuals Hybrid Silhouette and Key-Point Driven 3D-2D Registration Image modeling system development for robust descriptor of environmental change Effects of room reverberation on time-delay estimation performance Image Transformer MultiGrain: a unified image embedding for classes and instances How reverse reactions influence the yield of self-assembly robots An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection Shot aggregating strategy for near-duplicate video retrieval
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VCIP ICTD PCS NIPS Image Vis. Comput. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ISBI ICASSP ICIG (2) IEEE transactions on pattern analysis and machine intelligence ECCV ArXiv ECCV ArXiv ICLR ICIG FCV ICASSP (2) ICML ArXiv EUSIPCO
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