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.

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

Zhang, X.Y.[Xiang-Yu], Zou, J.H.[Jian-Hua], Ming, X.[Xiang], He, K.[Kaiming], 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., Gong, Y., Xiao, Z., Liu, Q.,
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, regression analysis, remote sensing, 2D reduction convolutional neural networks, USB-BBR algorithm, accurate object localization, dimension reduction model, elliptic Fourier transform based histogram, local binary pattern histogram Fourier, oriented gradients, region classification, region proposal, remote sensing images, unsupervised score based bounding box regression algorithm, Feature extraction, Neural networks, Object detection, Proposals, Remote sensing, Search methods, Semantics, Convolutional neural network (CNN), object localization, remote sensing images, unsupervised, score-based, bounding, box, regression, (USB-BBR) BibRef

Ouyang, W.L.[Wan-Li], Zeng, X.Y.[Xing-Yu], Wang, X.G.[Xiao-Gang], Qiu, S.[Shi], Luo, P.[Ping], Tian, Y.L.[Yong-Long], Li, H.S.[Hong-Sheng], Yang, S.[Shuo], Wang, Z.[Zhe], Li, H.Y.[Hong-Yang], Wang, K.[Kun], Yan, J.J.[Jun-Jie], Loy, C.C.[Chen-Change], Tang, X.[Xiaoou],
DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks,
PAMI(39), No. 7, July 2017, pp. 1320-1334.
IEEE DOI 1706
BibRef
Earlier: A1, A3, A2, A4, A5, A6, A7, A8, A9, A13, A14, Only:
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection,
CVPR15(2403-2412)
IEEE DOI 1510
Context modeling, Deformable models, Machine learning, Neural networks, Object detection, Training, Visualization, CNN, convolutional neural networks, deep learning, deep model, object detection. BibRef

Yang, L.J.[Lin-Jie], Liu, J.Z.[Jian-Zhuang], Tang, X.[Xiaoou],
Object Detection and Viewpoint Estimation with Auto-masking Neural Network,
ECCV14(III: 441-455).
Springer DOI 1408
BibRef

Wang, Z.[Zhe], Li, H.S.[Hong-Sheng], Ouyang, W.L.[Wan-Li], Wang, X.G.[Xiao-Gang],
Learnable Histogram: Statistical Context Features for Deep Neural Networks,
ECCV16(I: 246-262).
Springer DOI 1611
BibRef

Ouyang, W.L.[Wan-Li], Yang, X., Zhang, C., Yang, X.,
Factors in Finetuning Deep Model for Object Detection with Long-Tail Distribution,
CVPR16(864-873)
IEEE DOI 1612
BibRef

Wang, K., Lin, L., Zuo, W., Gu, S., Zhang, L.,
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection,
CVPR16(2138-2146)
IEEE DOI 1612
BibRef

Kang, K., Ouyang, W.L.[Wan-Li], Li, H.S.[Hong-Sheng], Wang, X.G.[Xiao-Gang],
Object Detection from Video Tubelets with Convolutional Neural Networks,
CVPR16(817-825)
IEEE DOI 1612
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


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],
Sparse Dictionary Learning for Identifying Grasp Locations,
WACV17(871-879)
IEEE DOI 1609
Dictionaries, Feature extraction, Grasping, Optimization, Standards, Three-dimensional displays, 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

Valipour, S., Siam, M., Jagersand, M., Ray, N.,
Recurrent Fully Convolutional Networks for Video Segmentation,
WACV17(29-36)
IEEE DOI 1609
Computer architecture, Deconvolution, Image segmentation, Logic gates, Neural networks, Semantics, Training 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], 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

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

Maninis, K.K.[Kevis-Kokitsi], Pont-Tuset, J.[Jordi], Arbeláez, P.[Pablo], Van Gool, L.J.[Luc J.],
Convolutional Oriented Boundaries,
ECCV16(I: 580-596).
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], Fan, Q.F.[Quan-Fu], Feris, R.S.[Rogerio S.], Vasconcelos, N.[Nuno],
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

Bochinski, E., Eiselein, V., Sikora, T.,
Training a convolutional neural network for multi-class object detection using solely virtual world data,
AVSS16(278-285)
IEEE DOI 1611
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

Oquab, M.[Maxime], Bottou, L.[Leon], Laptev, I.[Ivan], Sivic, J.[Josef],
Is object localization for free? - Weakly-supervised learning with convolutional neural networks,
CVPR15(685-694)
IEEE DOI 1510
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:Sep 18, 2017 at 11:34:11