He, S.F.[Sheng-Feng],
Lau, R.W.H.[Rynson W.H.],
Liu, W.[Wenxi],
Huang, Z.[Zhe],
Yang, Q.X.[Qing-Xiong],
SuperCNN: A Superpixelwise Convolutional Neural Network for Salient
Object Detection,
IJCV(115), No. 3, December 2015, pp. 330-344.
Springer DOI
1512
BibRef
Shin, H.C.,
Roth, H.R.,
Gao, M.,
Lu, L.,
Xu, Z.,
Nogues, I.,
Yao, J.,
Mollura, D.,
Summers, R.M.,
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN
Architectures, Dataset Characteristics and Transfer Learning,
MedImg(35), No. 5, May 2016, pp. 1285-1298.
IEEE DOI
1605
Biomedical imaging
BibRef
Li, X.,
Zhao, L.,
Wei, L.,
Yang, M.H.,
Wu, F.,
Zhuang, Y.,
Ling, H.,
Wang, J.,
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object
Detection,
IP(25), No. 8, August 2016, pp. 3919-3930.
IEEE DOI
1608
Computational modeling
BibRef
Zhang, X.Y.[Xiang-Yu],
Zou, J.H.[Jian-Hua],
He, K.M.[Kai-Ming],
Sun, J.[Jian],
Accelerating Very Deep Convolutional Networks for Classification and
Detection,
PAMI(38), No. 10, October 2016, pp. 1943-1955.
IEEE DOI
1609
Acceleration
BibRef
He, Y.,
Zhang, X.Y.[Xiang-Yu],
Sun, J.[Jian],
Channel Pruning for Accelerating Very Deep Neural Networks,
ICCV17(1398-1406)
IEEE DOI
1802
iterative methods, learning (artificial intelligence),
least squares approximations, neural nets, regression analysis,
Training
BibRef
Zhang, X.Y.[Xiang-Yu],
Zou, J.H.[Jian-Hua],
Ming, X.[Xiang],
He, K.M.[Kai-Ming],
Sun, J.[Jian],
Efficient and accurate approximations of nonlinear convolutional
networks,
CVPR15(1984-1992)
IEEE DOI
1510
BibRef
He, K.M.[Kai-Ming],
Zhang, X.Y.[Xiang-Yu],
Ren, S.Q.[Shao-Qing],
Sun, J.[Jian],
Delving Deep into Rectifiers:
Surpassing Human-Level Performance on ImageNet Classification,
ICCV15(1026-1034)
IEEE DOI
1602
Adaptation models
BibRef
He, K.M.[Kai-Ming],
Zhang, X.Y.[Xiang-Yu],
Ren, S.Q.[Shao-Qing],
Sun, J.[Jian],
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
Recognition,
PAMI(37), No. 9, September 2015, pp. 1904-1916.
IEEE DOI
1508
BibRef
Earlier:
ECCV14(III: 346-361).
Springer DOI
1408
Accuracy
BibRef
He, K.M.[Kai-Ming],
Zhang, X.Y.[Xiang-Yu],
Ren, S.Q.[Shao-Qing],
Sun, J.[Jian],
Deep Residual Learning for Image Recognition,
CVPR16(770-778)
IEEE DOI
1612
Award, CVPR.
BibRef
And:
Identity Mappings in Deep Residual Networks,
ECCV16(IV: 630-645).
Springer DOI
1611
BibRef
Ren, S.Q.[Shao-Qing],
He, K.M.[Kai-Ming],
Girshick, R.[Ross],
Sun, J.[Jian],
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
Networks,
PAMI(39), No. 6, June 2017, pp. 1137-1149.
IEEE DOI
1705
Convolutional codes, Detectors, Feature extraction,
Object detection, Proposals, Search problems, Training,
convolutional neural network, region proposal.
BibRef
Ren, S.Q.[Shao-Qing],
He, K.M.[Kai-Ming],
Girshick, R.[Ross],
Zhang, X.,
Sun, J.[Jian],
Object Detection Networks on Convolutional Feature Maps,
PAMI(39), No. 7, July 2017, pp. 1476-1481.
IEEE DOI
1706
Detectors, Electronic mail, Feature extraction, Object detection,
Proposals, Support vector machines, Training, CNN, Object detection,
convolutional, feature, map
BibRef
Wang, L.[Lei],
Zhang, B.C.[Bao-Chang],
Han, J.G.[Jun-Gong],
Shen, L.L.[Lin-Lin],
Qian, C.S.[Cheng-Shan],
Robust object representation by boosting-like deep learning
architecture,
SP:IC(47), No. 1, 2016, pp. 490-499.
Elsevier DOI
1610
Boosting
BibRef
Guo, H.,
Wang, J.,
Gao, Y.,
Li, J.,
Lu, H.,
Multi-View 3D Object Retrieval With Deep Embedding Network,
IP(25), No. 12, December 2016, pp. 5526-5537.
IEEE DOI
1612
convolution
BibRef
Rajchl, M.,
Lee, M.C.H.[M. C. H.],
Oktay, O.,
Kamnitsas, K.,
Passerat-Palmbach, J.,
Bai, W.,
Damodaram, M.,
Rutherford, M.A.,
Hajnal, J.V.,
Kainz, B.,
Rueckert, D.,
DeepCut: Object Segmentation From Bounding Box Annotations Using
Convolutional Neural Networks,
MedImg(36), No. 2, February 2017, pp. 674-683.
IEEE DOI
1702
Biological neural networks
BibRef
Wei, Y.,
Zhao, Y.,
Lu, C.,
Wei, S.,
Liu, L.,
Zhu, Z.,
Yan, S.,
Cross-Modal Retrieval With CNN Visual Features: A New Baseline,
Cyber(47), No. 2, February 2017, pp. 449-460.
IEEE DOI
1702
feature extraction
BibRef
Long, Y.[Yang],
Gong, Y.P.[Yi-Ping],
Xiao, Z.F.[Zhi-Feng],
Liu, Q.[Qing],
Accurate Object Localization in Remote Sensing Images Based on
Convolutional Neural Networks,
GeoRS(55), No. 5, May 2017, pp. 2486-2498.
IEEE DOI
1705
Fourier transforms, convolution, geophysical image processing,
image classification, neural nets, object detection,
accurate object localization, dimension reduction model,
BibRef
Wang, H.Z.[Hong-Zhen],
Wang, Y.[Ying],
Zhang, Q.[Qian],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Gated Convolutional Neural Network for Semantic Segmentation in
High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Afridi, M.J.[Muhammad Jamal],
Ross, A.[Arun],
Shapiro, E.M.[Erik M.],
On automated source selection for transfer learning in convolutional
neural networks,
PR(73), No. 1, 2018, pp. 65-75.
Elsevier DOI
1709
Transfer learning
BibRef
Xu, N.[Nuo],
Huo, C.L.[Chun-Lei],
Learning Deep Relationship for Object Detection,
IEICE(E101-D), No. 1, January 2018, pp. 273-276.
WWW Link.
1801
BibRef
Han, J.,
Zhang, D.,
Cheng, G.,
Liu, N.,
Xu, D.,
Advanced Deep-Learning Techniques for Salient and Category-Specific
Object Detection: A Survey,
SPMag(35), No. 1, January 2018, pp. 84-100.
IEEE DOI
1801
Survey, Deep Nets. Computer architecture, Computer vision, Convolution,
Feature extraction, Machine learning, Object detection, Visualization
BibRef
Xu, Z.Z.[Zhao-Zhuo],
Xu, X.[Xin],
Wang, L.[Lei],
Yang, R.[Rui],
Pu, F.L.[Fang-Ling],
Deformable ConvNet with Aspect Ratio Constrained NMS for Object
Detection in Remote Sensing Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link
1802
BibRef
Guo, W.[Wei],
Yang, W.[Wen],
Zhang, H.J.[Hai-Jian],
Hua, G.[Guang],
Geospatial Object Detection in High Resolution Satellite Images Based
on Multi-Scale Convolutional Neural Network,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link
1802
BibRef
Maninis, K.K.[Kevis-Kokitsi],
Pont-Tuset, J.[Jordi],
Arbeláez, P.[Pablo],
Van Gool, L.J.[Luc J.],
Convolutional Oriented Boundaries: From Image Segmentation to
High-Level Tasks,
PAMI(40), No. 4, April 2018, pp. 819-833.
IEEE DOI
1804
BibRef
Earlier:
Convolutional Oriented Boundaries,
ECCV16(I: 580-596).
Springer DOI
1611
computer vision, image classification, image representation,
image segmentation, neural nets, object detection, COB,
semantic contours
BibRef
Shi, W.W.[Wei-Wei],
Gong, Y.H.[Yi-Hong],
Cheng, D.[De],
Tao, X.Y.[Xiao-Yu],
Zheng, N.N.[Nan-Ning],
Entropy and orthogonality based deep discriminative feature learning
for object recognition,
PR(81), 2018, pp. 71-80.
Elsevier DOI
1806
Convolutional neural network (CNN),
Discriminative feature learning, Entropy, Orthogonality, Object recognition
BibRef
Bi, L.[Lei],
Feng, D.[Dagan],
Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network
(FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link.
1806
BibRef
Ding, P.[Peng],
Zhang, Y.[Ye],
Deng, W.J.[Wei-Jian],
Jia, P.[Ping],
Kuijper, A.[Arjan],
A light and faster regional convolutional neural network for object
detection in optical remote sensing images,
PandRS(141), 2018, pp. 208-218.
Elsevier DOI
1806
Deep convolution neural network, Deep learning (DL),
Remote sensing images, Object detection
BibRef
Hamaguchi, R.,
Fujita, A.,
Nemoto, K.,
Imaizumi, T.,
Hikosaka, S.,
Effective Use of Dilated Convolutions for Segmenting Small Object
Instances in Remote Sensing Imagery,
WACV18(1442-1450)
IEEE DOI
1806
feature extraction, geophysical image processing,
image segmentation, remote sensing, CNNs, LFE module,
Spatial resolution
BibRef
Simeoni, O.,
Iscen, A.,
Tolias, G.,
Avrithis, Y.,
Chum, O.,
Unsupervised Object Discovery for Instance Recognition,
WACV18(1745-1754)
IEEE DOI
1806
computer vision, feedforward neural nets, graph theory,
image representation, image retrieval, object detection,
BibRef
Ha, M.L.,
Franchi, G.,
Moller, M.,
Kolb, A.,
Blanz, V.,
Segmentation and Shape Extraction from Convolutional Neural Networks,
WACV18(1509-1518)
IEEE DOI
1806
convolution, feature extraction, feedforward neural nets,
image classification, image resolution, image segmentation,
Training
BibRef
Najibi, M.,
Yang, F.,
Wang, Q.,
Piramuthu, R.,
Towards the Success Rate of One:
Real-Time Unconstrained Salient Object Detection,
WACV18(1432-1441)
IEEE DOI
1806
Gaussian distribution, convolution, feedforward neural nets,
learning (artificial intelligence), object detection,
Visualization
BibRef
Wang, P.,
Chen, P.,
Yuan, Y.,
Liu, D.,
Huang, Z.,
Hou, X.,
Cottrell, G.,
Understanding Convolution for Semantic Segmentation,
WACV18(1451-1460)
IEEE DOI
1806
convolution, feedforward neural nets, image coding,
image resolution, image segmentation,
Training
BibRef
Ye, L.,
Liu, Z.,
Wang, Y.,
Learning Semantic Segmentation with Diverse Supervision,
WACV18(1461-1469)
IEEE DOI
1806
computer vision, feedforward neural nets, image classification,
image segmentation, learning (artificial intelligence),
Training
BibRef
Sobti, A.,
Arora, C.,
Balakrishnan, M.,
Object Detection in Real-Time Systems: Going Beyond Precision,
WACV18(1020-1028)
IEEE DOI
1806
computer vision, feedforward neural nets, object detection,
real-time systems, convolutional neural networks,
Robots
BibRef
Srivastava, S.,
Sharma, G.,
Lall, B.,
Large Scale Novel Object Discovery in 3D,
WACV18(179-188)
IEEE DOI
1806
feedforward neural nets, learning (artificial intelligence),
object detection, 3D convolutional neural network architecture,
Training
BibRef
Zagoruyko, S.[Sergey],
Komodakis, N.[Nikos],
Wide Residual Networks,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Zagoruyko, S.[Sergey],
Lerer, A.[Adam],
Lin, T.Y.[Tsung-Yi],
Pinheiro, P.O.[Pedro O.],
Gross, S.[Sam],
Chintala, S.[Soumith],
Dollar, P.[Piotr],
A MultiPath Network for Object Detection,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Tripathi, S.[Subarna],
Lipton, Z.[Zachary],
Belongie, S.[Serge],
Nguyen, T.[Truong],
Context Matters: Refining Object Detection in Video with Recurrent
Neural Networks,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Lou, Y.,
Fu, G.,
Jiang, Z.,
Men, A.,
Zhou, Y.,
Improve object detection via a multi-feature and multi-task CNN model,
VCIP17(1-4)
IEEE DOI
1804
convolution, feedforward neural nets, image resolution,
image segmentation, object detection, regression analysis,
Overlap Loss
BibRef
Han, G.,
Zhang, X.,
Li, C.,
Single shot object detection with top-down refinement,
ICIP17(3360-3364)
IEEE DOI
1803
Convolutional neural networks, Detectors, Feature extraction,
Object detection, Proposals, Semantics, Training,
top-down refinement
BibRef
Li, J.,
Qian, J.,
Yang, J.,
Object detection via feature fusion based single network,
ICIP17(3390-3394)
IEEE DOI
1803
Radio frequency, Dense box, Feature fusion,
Hierarchical feature, Object detection, Single network
BibRef
Yousif, H.,
He, Z.,
Kays, R.,
Object segmentation in the deep neural network feature domain from
highly cluttered natural scenes,
ICIP17(3095-3099)
IEEE DOI
1803
Animals, Computational modeling, Feature extraction,
Image representation, Image segmentation, Proposals, Semantics,
object detection
BibRef
Guo, Y.,
Guo, X.,
Jiang, Z.,
Zhou, Y.,
Cascaded convolutional neural networks for object detection,
VCIP17(1-4)
IEEE DOI
1804
image classification, neural nets, object detection,
binary classifier, convolutional neural networks,
Training
BibRef
Guo, Y.,
Guo, X.,
Jiang, Z.,
Men, A.,
Zhou, Y.,
Real-time object detection by a multi-feature fully convolutional
network,
ICIP17(670-674)
IEEE DOI
1803
Computer architecture, Feature extraction, Microprocessors,
Object detection, Proposals, Real-time systems, Semantics,
multi-feature
BibRef
Etemad, E.,
Gao, Q.,
Object localization by optimizing convolutional neural network
detection score using generic edge features,
ICIP17(675-679)
IEEE DOI
1803
Convolutional neural networks, Image edge detection,
Object recognition, Optimization, Proposals, Search problems,
RCNN
BibRef
Li, H.,
Yao, H.,
Hou, Y.,
Sun, X.,
Gated additive skip context connection for object detection,
ICIP17(680-684)
IEEE DOI
1803
Additives, Computational modeling, Context modeling,
Feature extraction, Logic gates, Object detection, Plugs,
object detection
BibRef
Wan, Z.,
He, H.,
Weakly supervised object localization with deep convolutional neural
network based on spatial pyramid saliency map,
ICIP17(4177-4181)
IEEE DOI
1803
Computer architecture, Computer vision,
Convolutional neural networks, Head, Labeling, Machine learning,
spatial pyramid saliency map
BibRef
Zhang, J.,
Li, B.,
Dai, Y.,
Porikli, F.,
He, M.,
Integrated deep and shallow networks for salient object detection,
ICIP17(1537-1541)
IEEE DOI
1803
Color, Convolutional neural networks, Machine learning,
Object detection, Saliency detection, Semantics, Training,
shallow network
BibRef
Li, T.,
Zhao, X.,
Cost efficient subcategory-aware CNN for object detection,
ICIP17(4202-4206)
IEEE DOI
1803
Automobiles, Benchmark testing, Heating systems, Neurons,
Object detection, Proposals,
Subcategory
BibRef
Rezatofighi, S.H.[S. Hamid],
Vijay Kumar, B.G.,
Milan, A.[Anton],
Abbasnejad, E.[Ehsan],
Dick, A.[Anthony],
Reid, I.D.[Ian D.],
DeepSetNet: Predicting Sets with Deep Neural Networks,
ICCV17(5257-5266)
IEEE DOI
1802
image classification,
learning (artificial intelligence), neural nets,
Random variables
BibRef
Gadde, R.[Raghudeep],
Jampani, V.[Varun],
Gehler, P.V.[Peter V.],
Semantic Video CNNs Through Representation Warping,
ICCV17(4463-4472)
IEEE DOI
1802
CNN for semantic segmentation into CNN for video data.
image sequences, video signal processing, video streaming,
Transforms
BibRef
Kim, D.,
Cho, D.,
Yoo, D.,
Two-Phase Learning for Weakly Supervised Object Localization,
ICCV17(3554-3563)
IEEE DOI
1802
convolution, image annotation, image segmentation,
inference mechanisms, learning (artificial intelligence),
Training
BibRef
Zhu, Y.,
Zhao, C.,
Wang, J.,
Zhao, X.,
Wu, Y.,
Lu, H.,
CoupleNet:
Coupling Global Structure with Local Parts for Object Detection,
ICCV17(4146-4154)
IEEE DOI
1802
convolution, image classification, neural nets, object detection,
Convolutional Neural Network detectors, CoupleNet,
Visualization
BibRef
Dvornik, N.,
Shmelkov, K.,
Mairal, J.,
Schmid, C.,
BlitzNet: A Real-Time Deep Network for Scene Understanding,
ICCV17(4174-4182)
IEEE DOI
1802
image segmentation,
learning (artificial intelligence), object detection,
Semantics
BibRef
Zhou, H.,
Li, Z.,
Ning, C.,
Tang, J.,
CAD: Scale Invariant Framework for Real-Time Object Detection,
AutoRob17(760-768)
IEEE DOI
1802
Convolution, Correlation, Detectors, Feature extraction,
Object detection, Real-time systems
BibRef
Shmelkov, K.,
Schmid, C.,
Alahari, K.,
Incremental Learning of Object Detectors without Catastrophic
Forgetting,
ICCV17(3420-3429)
IEEE DOI
1802
learning (artificial intelligence), neural nets,
object detection, COCO datasets, PASCAL VOC 2007, annotations,
Training data
BibRef
Zhang, R.[Rui],
Tang, S.[Sheng],
Zhang, Y.D.[Yong-Dong],
Li, J.T.[Jin-Tao],
Yan, S.C.[Shui-Cheng],
Scale-Adaptive Convolutions for Scene Parsing,
ICCV17(2050-2058)
IEEE DOI
1802
Scale adaptive to get both large and small regions.
regression analysis,
convolutional parameters, convolutional patches,
Training
BibRef
Shen, Z.Q.[Zhi-Qiang],
Liu, Z.[Zhuang],
Li, J.G.[Jian-Guo],
Jiang, Y.G.[Yu-Gang],
Chen, Y.R.[Yu-Rong],
Xue, X.Y.[Xiang-Yang],
DSOD: Learning Deeply Supervised Object Detectors from Scratch,
ICCV17(1937-1945)
IEEE DOI
1802
image classification,
learning (artificial intelligence), object detection, DSOD,
Training data
BibRef
Xu, H.,
Dong, M.,
Zhong, Z.,
Directionally Convolutional Networks for 3D Shape Segmentation,
ICCV17(2717-2726)
IEEE DOI
1802
convolution, image representation, image segmentation,
learning (artificial intelligence), shape recognition,
BibRef
He, K.,
Gkioxari, G.,
Dollár, P.,
Girshick, R.,
Mask R-CNN,
ICCV17(2980-2988)
IEEE DOI
1802
feature extraction, image segmentation, object detection,
pose estimation, Faster R-CNN, bounding-box object detection,
Semantics
BibRef
Chen, X.,
Zheng, A.,
Li, J.,
Lu, F.,
Look, Perceive and Segment: Finding the Salient Objects in Images via
Two-stream Fixation-Semantic CNNs,
ICCV17(1050-1058)
IEEE DOI
1802
feature extraction, image segmentation,
learning (artificial intelligence), object detection, SOD,
Streaming media
BibRef
He, S.,
Jiao, J.,
Zhang, X.,
Han, G.,
Lau, R.W.H.,
Delving into Salient Object Subitizing and Detection,
ICCV17(1059-1067)
IEEE DOI
1802
backpropagation, image representation,
neural nets, object detection, adaptive weight layer,
Training
BibRef
Liu, Y.,
Li, H.,
Yan, J.,
Wei, F.,
Wang, X.,
Tang, X.,
Recurrent Scale Approximation for Object Detection in CNN,
ICCV17(571-579)
IEEE DOI
1802
convolution, feature extraction, neural nets, object detection, RSA,
convolutional neural network, feature maps,
Prediction algorithms
BibRef
Moniruzzaman, M.[Mohammed],
Islam, S.M.S.[Syed Mohammed Shamsul],
Bennamoun, M.[Mohammed],
Lavery, P.[Paul],
Deep Learning on Underwater Marine Object Detection: A Survey,
ACIVS17(150-160).
Springer DOI
1712
BibRef
Bianco, S.[Simone],
Buzzelli, M.[Marco],
Schettini, R.[Raimondo],
A Fully Convolutional Network for Salient Object Detection,
CIAP17(II:82-92).
Springer DOI
1711
BibRef
Li, Q.Q.[Quan-Quan],
Jin, S.Y.[Sheng-Ying],
Yan, J.J.[Jun-Jie],
Mimicking Very Efficient Network for Object Detection,
CVPR17(7341-7349)
IEEE DOI
1711
Acceleration, Detectors, Feature extraction, Object detection,
Proposals, Training
BibRef
Azadi, S.[Samaneh],
Feng, J.S.[Jia-Shi],
Darrell, T.J.[Trevor J.],
Learning Detection with Diverse Proposals,
CVPR17(7369-7377)
IEEE DOI
1711
Computer architecture, Convolutional codes, Kernel,
Object detection, Proposals, Semantics, Training
BibRef
Kong, X.Y.[Xiang-Yu],
Xin, B.[Bo],
Wang, Y.Z.[Yi-Zhou],
Hua, G.[Gang],
Collaborative Deep Reinforcement Learning for Joint Object Search,
CVPR17(7072-7081)
IEEE DOI
1711
Bicycles, Collaboration, Learning (artificial intelligence),
Logic gates, Object detection, Proposals, Search, problems
BibRef
Jetley, S.[Saumya],
Sapienza, M.[Michael],
Golodetz, S.[Stuart],
Torr, P.H.S.[Philip H.S.],
Straight to Shapes: Real-Time Detection of Encoded Shapes,
CVPR17(4207-4216)
IEEE DOI
1711
Encoding, Object detection, Pipelines, Proposals, Real-time systems,
Shape, Training
BibRef
Hou, Q.,
Cheng, M.M.,
Hu, X.,
Borji, A.,
Tu, Z.,
Torr, P.H.S.[Philip H.S.],
Deeply Supervised Salient Object Detection with Short Connections,
CVPR17(5300-5309)
IEEE DOI
1711
Computer architecture, Feature extraction,
Image edge detection, Neural networks, Object, detection
BibRef
Jie, Z.,
Wei, Y.,
Jin, X.,
Feng, J.,
Liu, W.,
Deep Self-Taught Learning for Weakly Supervised Object Localization,
CVPR17(4294-4302)
IEEE DOI
1711
Correlation, Detectors, Feature extraction, Proposals, Reliability,
Support vector machines, Training
BibRef
Wu, B.,
Iandola, F.,
Jin, P.H.,
Keutzer, K.,
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural
Networks for Real-Time Object Detection for Autonomous Driving,
ECVW17(446-454)
IEEE DOI
1709
Computational modeling, Detectors, Feature extraction,
Object detection, Pipelines, Proposals, Real-time, systems
BibRef
Kudo, Y.[Yasunori],
Aoki, Y.[Yoshimitsu],
Dilated convolutions for image classification and object localization,
MVA17(452-455)
DOI Link
1708
Computer vision, Error analysis, Image recognition,
Image resolution, Image segmentation, Organizations, Pattern recognition
BibRef
Roh, M.C.,
Lee, J.Y.,
Refining faster-RCNN for accurate object detection,
MVA17(514-517)
DOI Link
1708
Art, Detectors, Licenses, Object detection, Organizations, Proposals, Training
BibRef
Brahmbhatt, S.[Samarth],
Christensen, H.I.[Henrik I.],
Hays, J.H.[James H.],
StuffNet: Using 'Stuff' to Improve Object Detection,
WACV17(934-943)
IEEE DOI
1609
Context, Feature extraction, Image segmentation, Object detection,
Proposals, Semantics, Training
BibRef
Xiang, Y.,
Choi, W.,
Lin, Y.,
Savarese, S.,
Subcategory-Aware Convolutional Neural Networks for Object Proposals
and Detection,
WACV17(924-933)
IEEE DOI
1609
Feature extraction, Heating systems, Object detection, Proposals,
Three-dimensional displays, Training, Two dimensional displays
BibRef
Trottier, L.[Ludovic],
Gigučre, P.[Philippe],
Chaib-Draa, B.[Brahim],
Convolutional Residual Network for Grasp Localization,
CRV17(168-175)
IEEE DOI
1804
BibRef
And:
Sparse Dictionary Learning for Identifying Grasp Locations,
WACV17(871-879)
IEEE DOI
1609
feedforward neural nets,
learning (artificial intelligence), manipulators, robot vision,
localization.
Dictionaries, Feature extraction, Grasping, Optimization, Standards,
Training
BibRef
Mercier, J.P.[Jean-Philippe],
Trottier, L.[Ludovic],
Gigučre, P.[Philippe],
Chaib-Draa, B.[Brahim],
Deep Object Ranking for Template Matching,
WACV17(734-742)
IEEE DOI
1609
Machine learning, Neural networks, Object detection, Robustness,
Service robots, Three-dimensional, displays
BibRef
Siam, M.,
Valipour, S.,
Jagersand, M.,
Ray, N.,
Convolutional gated recurrent networks for video segmentation,
ICIP17(3090-3094)
IEEE DOI
1803
BibRef
Earlier: A2, A1, A3, A4:
Recurrent Fully Convolutional Networks for Video Segmentation,
WACV17(29-36)
IEEE DOI
1609
Computer architecture, Deconvolution, Image segmentation,
Logic gates, Motion segmentation, Semantics, Training,
Video Semantic Segmentation.
Neural networks.
BibRef
Zhang, J.,
Dai, Y.,
Li, B.,
He, M.,
Attention to the Scale: Deep Multi-Scale Salient Object Detection,
DICTA17(1-7)
IEEE DOI
1804
feature extraction, image fusion,
learning (artificial intelligence), neural nets,
Semantics
BibRef
Zhang, J.,
Dai, Y.,
Porikli, F.M.[Fatih M.],
Deep Salient Object Detection by Integrating Multi-level Cues,
WACV17(1-10)
IEEE DOI
1609
Feature extraction, Image resolution, Image segmentation,
Machine learning, Neural networks, Object detection, Semantics
BibRef
Guo, S.X.[Shu-Xuan],
Liu, L.[Li],
Wang, W.[Wei],
Lao, S.Y.[Song-Yang],
Wang, L.[Liang],
An attention model based on spatial transformers for scene
recognition,
ICPR16(3757-3762)
IEEE DOI
1705
Databases, Feature extraction, Image recognition, Modeling,
Pattern recognition, Pipelines, Visualization
BibRef
Toca, C.,
Patrascu, C.,
Ciuc, M.,
AutoMarkov DNNs for object classification,
ICPR16(3452-3457)
IEEE DOI
1705
Biological neural networks, Computer architecture, Convolution,
Convolutional codes, Markov random fields, Neurons, Testing
BibRef
Sun, M.,
Han, T.X.,
Liu, M.C.[Ming-Chang],
Khodayari-Rostamabad, A.,
Multiple Instance Learning Convolutional Neural Networks for object
recognition,
ICPR16(3270-3275)
IEEE DOI
1705
Benchmark testing, Machine learning, Neural networks,
Object recognition, Optimization, Prediction algorithms, Training
BibRef
Shimoda, W.,
Yanai, K.,
Weakly-supervised segmentation by combining CNN feature maps and
object saliency maps,
ICPR16(1935-1940)
IEEE DOI
1705
Adaptation models, Convolution, Feature extraction,
Image segmentation, Proposals, Semantics, Training
BibRef
Sagawa, R.,
Shiba, Y.,
Hirukawa, T.,
Ono, S.,
Kawasaki, H.,
Furukawa, R.,
Automatic feature extraction using CNN for robust active one-shot
scanning,
ICPR16(234-239)
IEEE DOI
1705
Cameras, Decoding, Encoding, Image color analysis,
Image reconstruction, Shape, Three-dimensional, displays
BibRef
Wang, Z.[Ziqin],
Jiang, P.[Peilin],
Wang, F.[Fei],
Dense Residual Pyramid Networks for Salient Object Detection,
DeepVisual16(III: 606-621).
Springer DOI
1704
BibRef
Chen, C.[Chenyi],
Liu, M.Y.[Ming-Yu],
Tuzel, O.[Oncel],
Xiao, J.X.[Jian-Xiong],
R-CNN for Small Object Detection,
ACCV16(V: 214-230).
Springer DOI
1704
BibRef
Wang, Y.[Yida],
Cui, C.[Can],
Zhou, X.Z.[Xiu-Zhuang],
Deng, W.H.[Wei-Hong],
ZigzagNet:
Efficient Deep Learning for Real Object Recognition Based on 3D Models,
ACCV16(IV: 456-471).
Springer DOI
1704
BibRef
Nishida, K.[Kenshiro],
Hotta, K.[Kazuhiro],
Particle Detection in Crowd Regions Using Cumulative Score of CNN,
ISVC16(II: 566-575).
Springer DOI
1701
BibRef
Wang, C.,
Siddiqi, K.,
Differential Geometry Boosts Convolutional Neural Networks for Object
Detection,
DIFF-CV16(1006-1013)
IEEE DOI
1612
BibRef
Tran, D.[Du],
Bourdev, L.[Lubomir],
Fergus, R.[Rob],
Torresani, L.[Lorenzo],
Paluri, M.[Manohar],
Deep End2End Voxel2Voxel Prediction,
DeepLearn-C16(402-409)
IEEE DOI
1612
BibRef
Yang, B.[Bin],
Yan, J.J.[Jun-Jie],
Lei, Z.[Zhen],
Li, S.Z.[Stan Z.],
CRAFT Objects from Images,
CVPR16(6043-6051)
IEEE DOI
1612
CRAFT: Cascade Regionproposal-network And FasT-rcnn.
BibRef
Li, C.Y.[Chun-Yuan],
Stevens, A.[Andrew],
Chen, C.Y.[Chang-You],
Pu, Y.C.[Yun-Chen],
Gan, Z.[Zhe],
Carin, L.[Lawrence],
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape
Classification,
CVPR16(5666-5675)
IEEE DOI
1612
2d, 3D shape cues.
BibRef
Qi, C.R.[Charles R.],
Su, H.[Hao],
Nießner, M.[Matthias],
Dai, A.[Angela],
Yan, M.Y.[Meng-Yuan],
Guibas, L.J.[Leonidas J.],
Volumetric and Multi-view CNNs for Object Classification on 3D Data,
CVPR16(5648-5656)
IEEE DOI
1612
BibRef
Kuen, J.[Jason],
Wang, Z.H.[Zhen-Hua],
Wang, G.[Gang],
Recurrent Attentional Networks for Saliency Detection,
CVPR16(3668-3677)
IEEE DOI
1612
BibRef
Mathe, S.,
Pirinen, A.,
Sminchisescu, C.[Cristian],
Reinforcement Learning for Visual Object Detection,
CVPR16(2894-2902)
IEEE DOI
1612
BibRef
Misra, I.[Ishan],
Zitnick, C.L.[C. Lawrence],
Mitchell, M.[Margaret],
Girshick, R.[Ross],
Seeing through the Human Reporting Bias:
Visual Classifiers from Noisy Human-Centric Labels,
CVPR16(2930-2939)
IEEE DOI
1612
BibRef
Bell, S.[Sean],
Zitnick, C.L.[C. Lawrence],
Bala, K.[Kavita],
Girshick, R.[Ross],
Inside-Outside Net: Detecting Objects in Context with Skip Pooling
and Recurrent Neural Networks,
CVPR16(2874-2883)
IEEE DOI
1612
BibRef
Najibi, M.,
Rastegari, M.,
Davis, L.S.,
G-CNN: An Iterative Grid Based Object Detector,
CVPR16(2369-2377)
IEEE DOI
1612
BibRef
Borji, A.[Ali],
Izadi, S.[Saeed],
Itti, L.[Laurent],
iLab-20M:
A Large-Scale Controlled Object Dataset to Investigate Deep Learning,
CVPR16(2221-2230)
IEEE DOI
1612
Dataset, Learning.
BibRef
Chen, X.Z.[Xiao-Zhi],
Kundu, K.[Kaustav],
Zhu, Y.,
Ma, H.M.[Hui-Min],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
3D Object Proposals Using Stereo Imagery for Accurate Object Class
Detection,
PAMI(40), No. 5, May 2018, pp. 1259-1272.
IEEE DOI
1804
Context, Detectors, Laser radar, Object detection, Proposals,
Solid modeling, Three-dimensional displays, 3D object detection,
stereo
BibRef
Chen, X.Z.[Xiao-Zhi],
Kundu, K.[Kaustav],
Zhang, Z.Y.[Zi-Yu],
Ma, H.M.[Hui-Min],
Fidler, S.[Sanja],
Urtasun, R.[Raquel],
Monocular 3D Object Detection for Autonomous Driving,
CVPR16(2147-2156)
IEEE DOI
1612
BibRef
Yang, F.,
Choi, W.,
Lin, Y.,
Exploit All the Layers: Fast and Accurate CNN Object Detector with
Scale Dependent Pooling and Cascaded Rejection Classifiers,
CVPR16(2129-2137)
IEEE DOI
1612
BibRef
Hayder, Z.,
He, X.,
Salzmann, M.,
Learning to Co-Generate Object Proposals with a Deep Structured
Network,
CVPR16(2565-2573)
IEEE DOI
1612
BibRef
Jampani, V.[Varun],
Kiefel, M.[Martin],
Gehler, P.V.[Peter V.],
Learning Sparse High Dimensional Filters:
Image Filtering, Dense CRFs and Bilateral Neural Networks,
CVPR16(4452-4461)
IEEE DOI
1612
BibRef
Jampani, V.[Varun],
Gadde, R.[Raghudeep],
Gehler, P.V.[Peter V.],
Video Propagation Networks,
CVPR17(3154-3164)
IEEE DOI
1711
Computer architecture, Image color analysis, Lattices,
Optimization, Runtime, Semantics, Virtual, private, networks
BibRef
Gadde, R.[Raghudeep],
Jampani, V.[Varun],
Kiefel, M.[Martin],
Kappler, D.[Daniel],
Gehler, P.V.[Peter V.],
Superpixel Convolutional Networks Using Bilateral Inceptions,
ECCV16(I: 597-613).
Springer DOI
1611
BibRef
Wang, P.[Peng],
Yuille, A.L.[Alan L.],
DOC: Deep OCclusion Estimation from a Single Image,
ECCV16(I: 545-561).
Springer DOI
1611
BibRef
Wang, Y.,
Deng, W.,
Self-restraint object recognition by model based CNN learning,
ICIP16(654-658)
IEEE DOI
1610
Data models
BibRef
Liu, W.[Wei],
Anguelov, D.[Dragomir],
Erhan, D.[Dumitru],
Szegedy, C.[Christian],
Reed, S.[Scott],
Fu, C.Y.[Cheng-Yang],
Berg, A.C.[Alexander C.],
SSD: Single Shot MultiBox Detector,
ECCV16(I: 21-37).
Springer DOI
1611
BibRef
Cai, Z.W.[Zhao-Wei],
He, X.,
Sun, J.,
Vasconcelos, N.M.[Nuno M.],
Deep Learning with Low Precision by Half-Wave Gaussian Quantization,
CVPR17(5406-5414)
IEEE DOI
1711
Backpropagation, Biological neural networks, Complexity theory,
Computational modeling, Machine learning, Quantization, (signal)
BibRef
Cai, Z.W.[Zhao-Wei],
Fan, Q.F.[Quan-Fu],
Feris, R.S.[Rogerio S.],
Vasconcelos, N.M.[Nuno M.],
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object
Detection,
ECCV16(IV: 354-370).
Springer DOI
1611
BibRef
Tang, Y.[Youbao],
Wu, X.Q.[Xiang-Qian],
Saliency Detection via Combining Region-Level and Pixel-Level
Predictions with CNNs,
ECCV16(VIII: 809-825).
Springer DOI
1611
BibRef
Krusch, P.,
Bochinski, E.,
Eiselein, V.,
Sikora, T.,
A consistent two-level metric for evaluation of automated abandoned
object detection methods,
ICIP17(4352-4356)
IEEE DOI
1803
BibRef
Earlier: A2, A3, A4, Only:
Training a convolutional neural network for multi-class object
detection using solely virtual world data,
AVSS16(278-285)
IEEE DOI
1611
Cameras, Feature extraction, Lighting, Object detection, Protocols,
Volume measurement, abandoned object detection, evaluation, metric,
video surveillance.
Animals
BibRef
Cheng, K.W.,
Chen, Y.T.,
Fang, W.H.,
Iterative localization refinement in convolutional neural networks
for improved object detection,
ICIP16(3643-3647)
IEEE DOI
1610
Iterative methods
BibRef
Cervantes, E.,
Yu, L.L.,
Bagdanov, A.D.,
Masana, M.,
van de Weijer, J.,
Hierarchical part detection with deep neural networks,
ICIP16(1933-1937)
IEEE DOI
1610
Birds
BibRef
Kuo, W.,
Hariharan, B.,
Malik, J.,
DeepBox: Learning Objectness with Convolutional Networks,
ICCV15(2479-2487)
IEEE DOI
1602
Computer architecture
BibRef
Ma, C.H.[Chih-Hao],
Hsu, C.T.[Chiou-Ting],
Huet, B.[Benoit],
Nonparametric scene parsing with deep convolutional features and
dense alignment,
ICIP15(1915-1919)
IEEE DOI
1512
SIFT flow; deep convolutional network; object window; scene parsing
BibRef
Mopuri, K.R.[Konda Reddy],
Babu, R.V.[R. Venkatesh],
Object level deep feature pooling for compact image representation,
DeepLearn15(62-70)
IEEE DOI
1510
Computational modeling
BibRef
Caicedo, J.C.,
Lazebnik, S.[Svetlana],
Active Object Localization with Deep Reinforcement Learning,
ICCV15(2488-2496)
IEEE DOI
1602
Computational modeling
BibRef
Pepik, B.[Bojan],
Benenson, R.[Rodrigo],
Ritschel, T.[Tobias],
Schiele, B.[Bernt],
What Is Holding Back Convnets for Detection?,
GCPR15(517-528).
Springer DOI
1511
BibRef
Mrowca, D.,
Rohrbach, M.[Marcus],
Hoffman, J.,
Hu, R.,
Saenko, K.,
Darrell, T.J.,
Spatial Semantic Regularisation for Large Scale Object Detection,
ICCV15(2003-2011)
IEEE DOI
1602
Clustering algorithms
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Neural Networks Applied to Specific Problems .