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
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 .