Furthermore , it converges to a determined error goal at lower training epochs than an MLP . The performance of an MLP trained backpropagation and also fast backpropagation using adapted learning rates , an RBF net , and the proposed algorithm is compared using Iris plant database . The results show that the introduced algorithm is much better than the others in most cases , in terms of not only training epochs but also the number of hidden units and centres . The paper illustrated the rational method of choosing the width by the normal diffusion principle and the adνantage of choosing the center factor by clustering . The experimental results proνe that the RBF net designed in this way has superior performances to the general RBF net . It first derives the forward consistency from the raw historical observations . In contrast to mainstream video forecasting approaches , FB-Net then investigates the backward consistency from the future to the past to reinforce the predictions . The final predicted results are inferred by jointly taking both the forward and backward consistencies into account . Therefore , in this work , we propose a CNN network with feedback structure for Solar power plant detection on low-resolution satellite images . To express the strength of the top-down connections , we introduce feedback CNN network ( FB-Net ) to a baseline CNN model used for solar panel classification on multi-spectral satellite data . Moreover , we propose a class activation mapping method ( CAM ) to our FB-Net , which takes advantage of multi-channel pulse coupled neural network ( m-PCNN ) for pixel level detection of the solar power plants . We propose a novel light-weight previewer block , which previews the objectness probability for the potential regression region of each prior box , using the stronger features with larger receptive fields and more contextual information for better predictions . This previewer block is generic and can be easily implemented in multi-scale detectors , such as SSD , RFBNet and MS-CNN . Extensive experiments are conducted on PASCAL VOC and KITTI pedestrian benchmark to show the superiority of the proposed method . To overcome above shortcomings , we propose an end-to-end multi-task deep convolutional network model named ShuDA-RFBNet for multiple object detection and drivable area segmentation simultaneously . ShuDA-RFBNet adopts RFBNet for multiple object detection and DenseASPP for drivable area segmentation task . Both networks share a light-weight ShuffleNet V2 as the base net to extract features . The basic working theorem of BP net and RFB net are provided firstly , and then based on the problem of predicting the change of the stream way , the performances of the two nets are compared each other , at last the advantages and the disadvantages is presented briefly . To dispel this threat , in this paper we propose a privacy-preserving framework to effectively hide the information of user behaviors in wireless signals while retaining the ability of user authentication . The core of our framework is a novel Siamese network-based deep model , namely RFBP-Net . In this way , wireless sensing reveals user information moderately . We conduct extensive experiments on both the real WiFi and RFID system and open datasets . The experiment results show that RFBP-Net is able to significantly reduce the activity recognition accuracy , i.e. , 70 % reduction in the RFID system and 80 % reduction in the WiFi system , with a slight penalty in the user authentication accuracy , i.e. , only 5 % and 1 % decrease in the RFID and WiFi system , respectively . FSSD ( Feature Fusion Single Shot Multibox Detector ) proposed feature fusion module which can improve the performance significantly . RFB Net(Receptive Field Block Net for Accurate and Fast Object Detection ) proposed RFB module to simulate Receptive Fields ( RFs ) in human visual systems and gain higher accuracy . In this paper , we proposed FRFB Net ( Integrate Receptive Field Block Feature into Fusion Net for Single Shot Multibox Detector ) , an enhanced FSSD with a RFB module , which not only fully utilize the pyramidal features , but also change the RFs of the fused feature map . To make the model more robust , we use Gaussian Blur to process training images , in addition to use the data augmentation in SSD.On the Pascal VOC 2007 test , our network can achieve 79.6 mAP with the input size $ 300\times 300 Abstract This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems . The method uses linear programming ( LP ) models to train the RBF-like net . Polynomial time complexity of the method is proven and computational results are provided for many well-known problems . Inspired by the structure of Receptive Fields ( RFs ) in human visual systems , we propose a novel RF Block ( RFB ) module , which takes the relationship between the size and eccentricity of RFs into account , to enhance the feature discriminability and robustness . We further assemble RFB to the top of SSD , constructing the RFB Net detector . To evaluate its effectiveness , experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed . RFB algorithm is a very advanced algorithm in the direction of Object Detection based on Deep Learning , RFB Net proposed RFB module to simulate Receptive Fields ( RFs ) in human visual systems and gain higher accuracy . RFB , however , also have other Object Detection algorithm of faults , that is the detection of choosing predicted bounding box is a box with the highest scores , rather than the most close to the truth boundary box , and the positioning accuracy of bounding box is a core problem , therefore , we put forward a kind of method is replaced IoU(Intersection over Union ) with GIoU as predicting boundary box plus or minus a sample selection criteria which can improve the prediction location of bounding box , GIoU ( Generalized Intersection over Union ) is an improvement over IoU , based on the function of IoU , GIoU considers the offset angle , the distance of truth boundary box and predict boundary box to some extent . First , we utilize the relationships among target categories to model a hierarchical network to further improve the performance of recognition . Second , we combine bounding-box-level labeled images and image-level labeled images together for joint training , and the proposed method can be easily applied in current two-stage object detection framework with excellent performance . Experimental results show that the proposed large scale semi-supervised object detection network obtains the state-of-the-art performance , with the mAP of 38.1 % on the ImageNet detection validation dataset . Our method redefines RPN to a problem of minimizing the KL-divergence , difference between the two probability distributions . We applied KL-RPN , which performs region proposal using KL-Divergence , to the existing two-stage object detection framework and showed that it can improve the performance of the existing method . Experiments show that it achieves 2.6 % and 2.0 % AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone , respectively . However , the semantic information which is from the low-level features is less than that on the high-level features , and locations of objects on the low-level are more accurate than that on the high-level . Recently , feature pyramid fusion is widely applied by one-stage object detectors ( e.g. DSSD , RefineDet ) , which achieves encouraging results of the object detection , but only simply constructs the feature pyramid according to multi-scale feature maps . In our work , we design a network , called CFPNet , which applies cascade feature pyramid layer ( CFP ) to predict the classification and regression . Experimental results show that the proposed low complexity DNN models significantly reduce the number of parameters and computation complexity compared to the original VGG , GoogLeNet and ResNet-50 . The proposed DNN model is also used as feature extraction in the one-stage object detection method , Single Shot Detector ( SSD ) . This technique is inspired from curriculum learning . It is simple and effective and it is applicable to most single-stage object detectors . xNets map objects with similar sizes and aspect ratios into many specialized layers , allowing xNets to provide a scale and aspect ratio aware architecture . We leverage xNets to enhance single-stage object detection frameworks . First , we apply xNets on anchor-based object detection , for which we predict object centers and regress the top-left and bottom-right corners . Unlike prior work that uses knowledge distillation losses to maintain accuracy on original classes , this paper presents a simple and effective method for adding new class to the original deep object detectors while avoiding catastrophic forgetting . Our method is based on the framework of single-stage object detection . The core of our proposed solution is mining memory neurons in original model , then the rest neurons are updated and employed to detect new class . However , the accuracy of the neural network deteriorates after the normal quantization , especially for the object detection task . To overcome this dilemma , we propose a teacher – student based quantization training scheme for the one-stage object detection neural network . First , the quantization configurations of weights and activations are determined according to their statistical probability densities of each layer , thus ensuring a lower representation error of the fixed-point numeric . Application : Single-stage Object Detection Base model : MobileNet-v1 Framework : tensorflow1.1 Training Information : weights were fine-tuned using TF fake quantization nodes Quality : The COCO mAP(IoU=0.50:0.95 ) on 5000 validation images is 23.0 % We propose to use an anchor optimization method which will help reduce unnecessary region proposals as well as it can generate customized anchors depending upon the dataset . It can be used in conjunction with any single stage object detection framework . Its empirically noted that this anchor optimization technique improves accuracy over baseline frameworks . However , they can hardly surpass the accuracy of two - stage detectors . RetinaNet is the state of the art one - stage object detector that achieve comparable performance to two - stage detectors . It adopts an architecture modified from RPN and focuses on addressing the class imbalance during training . Fig . [ reference ] shows the distribution of from a converged one - stage detection model . Since the easy negatives have a dominant number , we use log axis to display the fraction of examples to demonstrate the details of the variance of examples with different attributes . Other works focus on incorporating sub - category information [ reference ] , generating object proposals at multiple scales with more contextual information [ reference ][ reference ][ reference ][ reference ] , selecting better features [ reference ] , improving speed , cascade procedure [ reference ] and better training procedure . section : One - stage object detectors On the other hand , YOLO and SSD [ reference ] have popularized the one - stage approach , which removes the RoI pooling step and detects objects in a single network . Figure 3 Overview . We adopt the popular two - stage object detection strategy that consists of region proposal and region classification . Rotational Region CNN ( R 2 CNN ) is based on Faster R - CNN [ reference ] . Figure 2 shows the architecture of R 2 CNN . Our results show that a vanilla implementation , without any bells and whistles , surpasses all previous state - of - the - art single - model detectors by a large margin , on the challenging COCO detection task [ reference ] , especially under the higher quality evaluation metrics . In addition , the Cascade R - CNN can be built with any two - stage object detector based on the R - CNN framework . We have observed consistent gains ( of 2∼4 points ) , at a marginal increase in computation . Our detector uses keypoint estimation to find center points and regresses to all other object properties , such as size , 3D location , orientation , and even pose . Our center point based approach , CenterNet , is end-to-end differentiable , simpler , faster , and more accurate than corresponding bounding box based detectors . CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset , with 28.1 % AP at 142 FPS , 37.4 % AP at 52 FPS , and 45.1 % AP with multi-scale testing at 1.4 FPS . In this paper , we propose the use of object detection techniques in speech processing tasks . We selected two state-of-the-art object detectors , namely YOLO and CenterNet , based on a trade-off between detection accuracy and speed . We tackled the problem of phoneme sequence recognition using three systems : the domain transfer learning system ( DTS ) from image to speech , intra-language transfer leaning system ( IaTS ) between speech corpora within the same language ( English to English ) , and inter-language transfer learning system ( IeTS ) between speech corpora from dissimilar languages ( English to Arabic ) . For English phoneme recognition , the Texas Instruments/Massachusetts Institute of Technology ( TIMIT ) corpus is used to evaluate the performance of the proposed systems . Our IaTS based on the CenterNet detector achieves the best results using the test core set of TIMIT with 15.89 % phone error rate ( PER ) . For Arabic phoneme recognition , the best performance , with 7.58 % PER , was achieved using the CenterNet . In our single-shot detection method , the recall-priority branch is based on an anchor-free module without non-maximum suppression ( NMS ) , while the precision-priority branch utilizes an anchor-based module with NMS . We perform recall-priority branch functions based on the output part of the CenterNet object detector to precisely predict center points of bounding boxes . The Bidirectional Feature Pyramid Network ( BiFPN ) , combined with the inference part of YOLOv3 , is used to improve the precision of precision-priority branch . To address this challenge , we concentrate on the combination of coarse semantic information and a feature-based SLAM system . In this article , a modified CenterNet object detector is proposed as the moving object detection thread for providing coarse semantic information and 2D location . The modified CenterNet can provide a faster and more accurate prediction . The main objective of this work is to present the methodology for designing an intelligent human-machine interface for a commercial joystick controlled assistive robotic arm realizing shared autonomy and supervisory control modes . Preliminary results on a RGB-D based object detection and position estimation system development using publicly available YOLOv3 and CenterNet deep learning models implementation of an autonomous object grasping mode by the Kinova Jaco robotic arm are described in detail and experimentally demonstrated . On CIFAR-100 and Tiny ImageNet , when applying the WHE layer to ResNet models , it demonstrates consistent improvement in the accuracy of the networks . Applying the WHE layer to ResNet backbone of the CenterNet object detection model can also boost its performance on COCO and Pascal VOC datasets .
Furthermore , it converges to a determined error goal at lower training epochs than an MLP . The performance of an MLP trained backpropagation and also fast backpropagation using adapted learning rates , an RBF net , and the proposed algorithm is compared using Iris plant database . The results show that the introduced algorithm is much better than the others in most cases , in terms of not only training epochs but also the number of hidden units and centres .
The paper illustrated the rational method of choosing the width by the normal diffusion principle and the adνantage of choosing the center factor by clustering . The experimental results proνe that the RBF net designed in this way has superior performances to the general RBF net .
It first derives the forward consistency from the raw historical observations . In contrast to mainstream video forecasting approaches , FB-Net then investigates the backward consistency from the future to the past to reinforce the predictions . The final predicted results are inferred by jointly taking both the forward and backward consistencies into account .
Therefore , in this work , we propose a CNN network with feedback structure for Solar power plant detection on low-resolution satellite images . To express the strength of the top-down connections , we introduce feedback CNN network ( FB-Net ) to a baseline CNN model used for solar panel classification on multi-spectral satellite data . Moreover , we propose a class activation mapping method ( CAM ) to our FB-Net , which takes advantage of multi-channel pulse coupled neural network ( m-PCNN ) for pixel level detection of the solar power plants .
We propose a novel light-weight previewer block , which previews the objectness probability for the potential regression region of each prior box , using the stronger features with larger receptive fields and more contextual information for better predictions . This previewer block is generic and can be easily implemented in multi-scale detectors , such as SSD , RFBNet and MS-CNN . Extensive experiments are conducted on PASCAL VOC and KITTI pedestrian benchmark to show the superiority of the proposed method .
To overcome above shortcomings , we propose an end-to-end multi-task deep convolutional network model named ShuDA-RFBNet for multiple object detection and drivable area segmentation simultaneously . ShuDA-RFBNet adopts RFBNet for multiple object detection and DenseASPP for drivable area segmentation task . Both networks share a light-weight ShuffleNet V2 as the base net to extract features .
The basic working theorem of BP net and RFB net are provided firstly , and then based on the problem of predicting the change of the stream way , the performances of the two nets are compared each other , at last the advantages and the disadvantages is presented briefly .
To dispel this threat , in this paper we propose a privacy-preserving framework to effectively hide the information of user behaviors in wireless signals while retaining the ability of user authentication . The core of our framework is a novel Siamese network-based deep model , namely RFBP-Net . In this way , wireless sensing reveals user information moderately . We conduct extensive experiments on both the real WiFi and RFID system and open datasets . The experiment results show that RFBP-Net is able to significantly reduce the activity recognition accuracy , i.e. , 70 % reduction in the RFID system and 80 % reduction in the WiFi system , with a slight penalty in the user authentication accuracy , i.e. , only 5 % and 1 % decrease in the RFID and WiFi system , respectively .
FSSD ( Feature Fusion Single Shot Multibox Detector ) proposed feature fusion module which can improve the performance significantly . RFB Net(Receptive Field Block Net for Accurate and Fast Object Detection ) proposed RFB module to simulate Receptive Fields ( RFs ) in human visual systems and gain higher accuracy . In this paper , we proposed FRFB Net ( Integrate Receptive Field Block Feature into Fusion Net for Single Shot Multibox Detector ) , an enhanced FSSD with a RFB module , which not only fully utilize the pyramidal features , but also change the RFs of the fused feature map . To make the model more robust , we use Gaussian Blur to process training images , in addition to use the data augmentation in SSD.On the Pascal VOC 2007 test , our network can achieve 79.6 mAP with the input size $ 300\times 300
Abstract This paper presents a new algorithm for generating radial basis function (RBF)-like nets for classification problems . The method uses linear programming ( LP ) models to train the RBF-like net . Polynomial time complexity of the method is proven and computational results are provided for many well-known problems .
Inspired by the structure of Receptive Fields ( RFs ) in human visual systems , we propose a novel RF Block ( RFB ) module , which takes the relationship between the size and eccentricity of RFs into account , to enhance the feature discriminability and robustness . We further assemble RFB to the top of SSD , constructing the RFB Net detector . To evaluate its effectiveness , experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed .
RFB algorithm is a very advanced algorithm in the direction of Object Detection based on Deep Learning , RFB Net proposed RFB module to simulate Receptive Fields ( RFs ) in human visual systems and gain higher accuracy . RFB , however , also have other Object Detection algorithm of faults , that is the detection of choosing predicted bounding box is a box with the highest scores , rather than the most close to the truth boundary box , and the positioning accuracy of bounding box is a core problem , therefore , we put forward a kind of method is replaced IoU(Intersection over Union ) with GIoU as predicting boundary box plus or minus a sample selection criteria which can improve the prediction location of bounding box , GIoU ( Generalized Intersection over Union ) is an improvement over IoU , based on the function of IoU , GIoU considers the offset angle , the distance of truth boundary box and predict boundary box to some extent .
First , we utilize the relationships among target categories to model a hierarchical network to further improve the performance of recognition . Second , we combine bounding-box-level labeled images and image-level labeled images together for joint training , and the proposed method can be easily applied in current two-stage object detection framework with excellent performance . Experimental results show that the proposed large scale semi-supervised object detection network obtains the state-of-the-art performance , with the mAP of 38.1 % on the ImageNet detection validation dataset .
Our method redefines RPN to a problem of minimizing the KL-divergence , difference between the two probability distributions . We applied KL-RPN , which performs region proposal using KL-Divergence , to the existing two-stage object detection framework and showed that it can improve the performance of the existing method . Experiments show that it achieves 2.6 % and 2.0 % AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone , respectively .
However , the semantic information which is from the low-level features is less than that on the high-level features , and locations of objects on the low-level are more accurate than that on the high-level . Recently , feature pyramid fusion is widely applied by one-stage object detectors ( e.g. DSSD , RefineDet ) , which achieves encouraging results of the object detection , but only simply constructs the feature pyramid according to multi-scale feature maps . In our work , we design a network , called CFPNet , which applies cascade feature pyramid layer ( CFP ) to predict the classification and regression .
Experimental results show that the proposed low complexity DNN models significantly reduce the number of parameters and computation complexity compared to the original VGG , GoogLeNet and ResNet-50 . The proposed DNN model is also used as feature extraction in the one-stage object detection method , Single Shot Detector ( SSD ) .
This technique is inspired from curriculum learning . It is simple and effective and it is applicable to most single-stage object detectors .
xNets map objects with similar sizes and aspect ratios into many specialized layers , allowing xNets to provide a scale and aspect ratio aware architecture . We leverage xNets to enhance single-stage object detection frameworks . First , we apply xNets on anchor-based object detection , for which we predict object centers and regress the top-left and bottom-right corners .
Unlike prior work that uses knowledge distillation losses to maintain accuracy on original classes , this paper presents a simple and effective method for adding new class to the original deep object detectors while avoiding catastrophic forgetting . Our method is based on the framework of single-stage object detection . The core of our proposed solution is mining memory neurons in original model , then the rest neurons are updated and employed to detect new class .
However , the accuracy of the neural network deteriorates after the normal quantization , especially for the object detection task . To overcome this dilemma , we propose a teacher – student based quantization training scheme for the one-stage object detection neural network . First , the quantization configurations of weights and activations are determined according to their statistical probability densities of each layer , thus ensuring a lower representation error of the fixed-point numeric .
Application : Single-stage Object Detection Base model : MobileNet-v1 Framework : tensorflow1.1 Training Information : weights were fine-tuned using TF fake quantization nodes Quality : The COCO mAP(IoU=0.50:0.95 ) on 5000 validation images is 23.0 %
We propose to use an anchor optimization method which will help reduce unnecessary region proposals as well as it can generate customized anchors depending upon the dataset . It can be used in conjunction with any single stage object detection framework . Its empirically noted that this anchor optimization technique improves accuracy over baseline frameworks .
However , they can hardly surpass the accuracy of two - stage detectors . RetinaNet is the state of the art one - stage object detector that achieve comparable performance to two - stage detectors . It adopts an architecture modified from RPN and focuses on addressing the class imbalance during training .
Fig . [ reference ] shows the distribution of from a converged one - stage detection model . Since the easy negatives have a dominant number , we use log axis to display the fraction of examples to demonstrate the details of the variance of examples with different attributes .
Other works focus on incorporating sub - category information [ reference ] , generating object proposals at multiple scales with more contextual information [ reference ][ reference ][ reference ][ reference ] , selecting better features [ reference ] , improving speed , cascade procedure [ reference ] and better training procedure . section : One - stage object detectors On the other hand , YOLO and SSD [ reference ] have popularized the one - stage approach , which removes the RoI pooling step and detects objects in a single network .
Figure 3 Overview . We adopt the popular two - stage object detection strategy that consists of region proposal and region classification . Rotational Region CNN ( R 2 CNN ) is based on Faster R - CNN [ reference ] . Figure 2 shows the architecture of R 2 CNN .
Our results show that a vanilla implementation , without any bells and whistles , surpasses all previous state - of - the - art single - model detectors by a large margin , on the challenging COCO detection task [ reference ] , especially under the higher quality evaluation metrics . In addition , the Cascade R - CNN can be built with any two - stage object detector based on the R - CNN framework . We have observed consistent gains ( of 2∼4 points ) , at a marginal increase in computation .
Our detector uses keypoint estimation to find center points and regresses to all other object properties , such as size , 3D location , orientation , and even pose . Our center point based approach , CenterNet , is end-to-end differentiable , simpler , faster , and more accurate than corresponding bounding box based detectors . CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset , with 28.1 % AP at 142 FPS , 37.4 % AP at 52 FPS , and 45.1 % AP with multi-scale testing at 1.4 FPS .
In this paper , we propose the use of object detection techniques in speech processing tasks . We selected two state-of-the-art object detectors , namely YOLO and CenterNet , based on a trade-off between detection accuracy and speed . We tackled the problem of phoneme sequence recognition using three systems : the domain transfer learning system ( DTS ) from image to speech , intra-language transfer leaning system ( IaTS ) between speech corpora within the same language ( English to English ) , and inter-language transfer learning system ( IeTS ) between speech corpora from dissimilar languages ( English to Arabic ) . For English phoneme recognition , the Texas Instruments/Massachusetts Institute of Technology ( TIMIT ) corpus is used to evaluate the performance of the proposed systems . Our IaTS based on the CenterNet detector achieves the best results using the test core set of TIMIT with 15.89 % phone error rate ( PER ) . For Arabic phoneme recognition , the best performance , with 7.58 % PER , was achieved using the CenterNet .
In our single-shot detection method , the recall-priority branch is based on an anchor-free module without non-maximum suppression ( NMS ) , while the precision-priority branch utilizes an anchor-based module with NMS . We perform recall-priority branch functions based on the output part of the CenterNet object detector to precisely predict center points of bounding boxes . The Bidirectional Feature Pyramid Network ( BiFPN ) , combined with the inference part of YOLOv3 , is used to improve the precision of precision-priority branch .
To address this challenge , we concentrate on the combination of coarse semantic information and a feature-based SLAM system . In this article , a modified CenterNet object detector is proposed as the moving object detection thread for providing coarse semantic information and 2D location . The modified CenterNet can provide a faster and more accurate prediction .
The main objective of this work is to present the methodology for designing an intelligent human-machine interface for a commercial joystick controlled assistive robotic arm realizing shared autonomy and supervisory control modes . Preliminary results on a RGB-D based object detection and position estimation system development using publicly available YOLOv3 and CenterNet deep learning models implementation of an autonomous object grasping mode by the Kinova Jaco robotic arm are described in detail and experimentally demonstrated .
On CIFAR-100 and Tiny ImageNet , when applying the WHE layer to ResNet models , it demonstrates consistent improvement in the accuracy of the networks . Applying the WHE layer to ResNet backbone of the CenterNet object detection model can also boost its performance on COCO and Pascal VOC datasets .
Improved backpropagation training algorithm using conic section functions ANALYSIS AND DESIGN OF RBF NEURAL NET BASED ON INFORMATION DIFFUSION Video Forecasting with Forward-Backward-Net: Delving Deeper into Spatiotemporal Consistency Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Convolutional Neural Networks with Feedback Model and m-PCNN Fusion Previewer for Multi-Scale Object Detector ShuDA-RFBNet for Real-time Multi-task Traffic Scene Perception Comparisons between BP Net and RFB Net in Prediction Problems A Framework for Behavior Privacy Preserving in Radio Frequency Signal FRFB: Integrate Receptive Field Block Into Feature Fusion Net for Single Shot Multibox Detector An algorithm to generate radial basis function (RBF)-like nets for classification problems Receptive Field Block Net for Accurate and Fast Object Detection Introduce GIoU into RFB net to optimize object detection bounding box Hierarchical Structure and Joint Training for Large Scale Semi-supervised Object Detection KL-Divergence-Based Region Proposal Network for Object Detection Cascade Feature Pyramid Neural Nwtwork for Object Detector Low-Complexity Deep Neural Networks for Image Object Classification and Detection Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection Incremental Learning of Single-stage Detectors with Mining Memory Neurons Training quantized one-stage object detection neural networks via selective feature imitation mobilenet-v1-ssd300.tensorflow (8bit quantized and fine-tuned) Small Object Detection in Aerial Imagery using RetinaNet with Anchor Optimization Gradient Harmonized Single-stage Detector CornerNet: Detecting Objects as Paired Keypoints R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Cascade R-CNN: Delving Into High Quality Object Detection Objects as Points Towards Deep Object Detection Techniques for Phoneme Recognition Priority Branches for Ship Detection in Optical Remote Sensing Images Coarse Semantic-Based Motion Removal for Robust Mapping in Dynamic Environments Autonomous Object Detection and Grasping Using Deep Learning for Design of an Intelligent Assistive Robot Manipulation System Wide Hidden Expansion Layer for Deep Convolutional Neural Networks
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