14.5.7.5.2 Convolutional Neural Networks for Object Detection and Segmentation

Chapter Contents (Back)
Convolutional Neural Networks. CNN. Neural Networks. Deep Nets. CNN for Image Descriptions. See also Salient Regions, Saliencey for Regions. See also Adversarial Networks, Adversarial Inputs.

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

Zeng, X.Y.[Xing-Yu], Ouyang, W.L.[Wan-Li], Yan, J.J.[Jun-Jie], Li, H.S.[Hong-Sheng], Xiao, T.[Tong], Wang, K.[Kun], Liu, Y.[Yu], Zhou, Y.C.[Yu-Cong], Yang, B.[Bin], Wang, Z.[Zhe], Zhou, H.[Hui], Wang, X.G.[Xiao-Gang],
Crafting GBD-Net for Object Detection,
PAMI(40), No. 9, September 2018, pp. 2109-2123.
IEEE DOI 1808
gated bi-directional CNN. Object detection, Rabbits, Visualization, Feature extraction, Head, Proposals, Logic gates, Convolutional neural network, CNN, object detection BibRef

Pang, S.M.[Shan-Min], Zhu, J.H.[Ji-Hua], Wang, J.X.[Jia-Xing], Ordonez, V.[Vicente], Xue, J.R.[Jian-Ru],
Building discriminative CNN image representations for object retrieval using the replicator equation,
PR(83), 2018, pp. 150-160.
Elsevier DOI 1808
Object retrieval, Replicator equation, Deep feature selection, Deep feature weighting BibRef


Ouadiay, F.Z., Bouftaih, H., Bouyakhf, E.H., Himmi, M.M.,
Simultaneous object detection and localization using convolutional neural networks,
ISCV18(1-8)
IEEE DOI 1807
computer vision, convolution, feature extraction, feedforward neural nets, image classification, object detection BibRef

Amin, S.[Sikandar], Galasso, F.[Fabio],
Geometric proposals for faster R-CNN,
AVSS17(1-6)
IEEE DOI 1806
convolution, geometry, neural nets, object detection, object tracking, video signal processing, video surveillance, Training 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 .


Last update:Aug 16, 2018 at 18:22:30