14.5.7.5 Convolutional Neural Networks for Image Descriptions, Deep Nets

Chapter Contents (Back)
Convolutional Neural Networks. CNN. Neural Networks. Deep Nets. CNN for Image Descriptions. Implementation issues: See also Convolutional Neural Networks Implementation Issues.

Bengio, Y.,
Learning deep architectures for AI,
FTML(1), No. 1, 2009, pp. 1-127.
DOI Link 1609
BibRef

Farabet, C.[Clement], Couprie, C.[Camille], Najman, L.[Laurent], Le Cun, Y.L.[Yann L.],
Learning Hierarchical Features for Scene Labeling,
PAMI(35), No. 8, 2013, pp. 1915-1929.
IEEE DOI 1307
Image edge detection; Convolutional networks; deep learning; scene parsing BibRef

Abdulnabi, A.H., Wang, G., Lu, J., Jia, K.,
Multi-Task CNN Model for Attribute Prediction,
MultMed(17), No. 11, November 2015, pp. 1949-1959.
IEEE DOI 1511
Clothing BibRef

Hu, F.[Fan], Xia, G.S.[Gui-Song], Hu, J.W.[Jing-Wen], Zhang, L.P.[Liang-Pei],
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14680.
DOI Link 1512
BibRef

Perlin, H.A.[Hugo Alberto], Lopes, H.S.[Heitor Silvério],
Extracting human attributes using a convolutional neural network approach,
PRL(68, Part 2), No. 1, 2015, pp. 250-259.
Elsevier DOI 1512
Computer vision BibRef

Zhao, W.[Wenzhi], Du, S.H.[Shi-Hong],
Learning multiscale and deep representations for classifying remotely sensed imagery,
PandRS(113), No. 1, 2016, pp. 155-165.
Elsevier DOI 1602
Multiscale convolutional neural network (MCNN) BibRef

Barat, C.[Cécile], Ducottet, C.[Christophe],
String representations and distances in deep Convolutional Neural Networks for image classification,
PR(54), No. 1, 2016, pp. 104-115.
Elsevier DOI 1603
Convolutional Neural Network BibRef

Zhang, F., Du, B., Zhang, L.,
Scene Classification via a Gradient Boosting Random Convolutional Network Framework,
GeoRS(54), No. 3, March 2016, pp. 1793-1802.
IEEE DOI 1603
Boosting BibRef

Liang, H.M.[He-Ming], Li, Q.[Qi],
Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features,
RS(8), No. 2, 2016, pp. 99.
DOI Link 1603
BibRef

Zuo, Z.[Zhen], Shuai, B.[Bing], Wang, G.[Gang], Liu, X.[Xiao], Wang, X.X.[Xing-Xing], Wang, B.[Bing], Chen, Y.S.[Yu-Shi],
Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks,
IP(25), No. 7, July 2016, pp. 2983-2996.
IEEE DOI 1606
BibRef
Earlier:
Convolutional recurrent neural networks: Learning spatial dependencies for image representation,
DeepLearn15(18-26)
IEEE DOI 1510
computational complexity. Computational modeling BibRef

Greenspan, H., van Ginneken, B., Summers, R.M.,
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique,
MedImg(35), No. 5, May 2016, pp. 1153-1159.
IEEE DOI 1605
Artificial neural networks BibRef

Ghesu, F.C.[Florin C.], Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J.[Joachim], Comaniciu, D.,
Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing,
MedImg(35), No. 5, May 2016, pp. 1217-1228.
IEEE DOI 1605
Context BibRef

Murthy, V.N., Singh, V., Chen, T., Manmatha, R., Comaniciu, D.,
Deep Decision Network for Multi-class Image Classification,
CVPR16(2240-2248)
IEEE DOI 1612
BibRef

Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M.B., Liang, J.,
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,
MedImg(35), No. 5, May 2016, pp. 1299-1312.
IEEE DOI 1605
Biomedical imaging BibRef

Shin, J.Y., Tajbakhsh, N., Hurst, R.T., Kendall, C.B., Liang, J.,
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks,
CVPR16(2526-2535)
IEEE DOI 1612
BibRef

Mansoor, A., Cerrolaza, J.J., Idrees, R., Biggs, E., Alsharid, M.A., Avery, R.A., Linguraru, M.G.,
Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation,
MedImg(35), No. 8, August 2016, pp. 1856-1865.
IEEE DOI 1608
Biomedical optical imaging BibRef

Wei, Y.C.[Yun-Chao], Xia, W.[Wei], Lin, M.[Min], Huang, J.S.[Jun-Shi], Ni, B.B.[Bing-Bing], Dong, J.[Jian], Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
HCP: A Flexible CNN Framework for Multi-Label Image Classification,
PAMI(38), No. 9, September 2016, pp. 1901-1907.
IEEE DOI 1609
image classification BibRef

Bu, S.H.[Shu-Hui], Han, P.C.[Peng-Cheng], Liu, Z.B.[Zhen-Bao], Han, J.W.[Jun-Wei],
Scene parsing using inference Embedded Deep Networks,
PR(59), No. 1, 2016, pp. 188-198.
Elsevier DOI 1609
Convolutional Neural Networks (CNNs) BibRef

Liu, N.[Nian], Han, J.W.[Jun-Wei],
DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection,
CVPR16(678-686)
IEEE DOI 1612
BibRef

Cai, X.X.[Xiu-Xia], Song, B.[Bin],
Combining inconsistent textures using convolutional neural networks,
JVCIR(40, Part A), No. 1, 2016, pp. 366-375.
Elsevier DOI 1609
Large-scale bound-constrained optimization BibRef

van Noord, N.[Nanne], Postma, E.[Eric],
Learning scale-variant and scale-invariant features for deep image classification,
PR(61), No. 1, 2017, pp. 583-592.
Elsevier DOI 1609
Convolutional Neural Networks BibRef

Du, J.[Jun], Xu, Y.[Yong],
Hierarchical deep neural network for multivariate regression,
PR(63), No. 1, 2017, pp. 149-157.
Elsevier DOI 1612
Divide and Conquer BibRef

Nanni, L.[Loris], Ghidoni, S.[Stefano],
How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?,
PRL(85), No. 1, 2017, pp. 1-7.
Elsevier DOI 1612
Deep convolutional neural networks BibRef

Paulin, M.[Mattis], Mairal, J.[Julien], Douze, M.[Matthijs], Harchaoui, Z.[Zaid], Perronnin, F.[Florent], Schmid, C.[Cordelia],
Convolutional Patch Representations for Image Retrieval: An Unsupervised Approach,
IJCV(121), No. 1, January 2017, pp. 149-168.
Springer DOI 1702
BibRef
Earlier: A1, A3, A4, A2, A5, A6:
Local Convolutional Features with Unsupervised Training for Image Retrieval,
ICCV15(91-99)
IEEE DOI 1602
Computer architecture BibRef

Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.,
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification,
GeoRS(55), No. 2, February 2017, pp. 645-657.
IEEE DOI 1702
geophysical image processing BibRef

Li, Y.[Ying], Zhang, H.[Haokui], Shen, Q.A.[Qi-Ang],
Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Guo, S., Huang, W., Wang, L., Qiao, Y.,
Locally Supervised Deep Hybrid Model for Scene Recognition,
IP(26), No. 2, February 2017, pp. 808-820.
IEEE DOI 1702
data compression BibRef

Wang, Z.[Zhe], Wang, L.M.[Li-Min], Wang, Y.L.[Ya-Li], Zhang, B.[Bowen], Qiao, Y.[Yu],
Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition,
IP(26), No. 4, April 2017, pp. 2028-2041.
IEEE DOI 1704
Dictionaries BibRef

Masoumi, M.[Majid], Hamza, A.B.[A. Ben],
Spectral shape classification: A deep learning approach,
JVCIR(43), No. 1, 2017, pp. 198-211.
Elsevier DOI 1702
Deep learning BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mining Mid-level Visual Patterns with Deep CNN Activations,
IJCV(121), No. 3, February 2017, pp. 344-364.
Springer DOI 1702
BibRef

Revathi, A.R., Kumar, D.[Dhananjay],
An efficient system for anomaly detection using deep learning classifier,
SIViP(11), No. 2, February 2017, pp. 291-299.
WWW Link. 1702
BibRef

Saint Andre, M.D.[Matthieu De_La_Roche], Rieger, L.[Laura], Hannemose, M.[Morten], Kim, J.[Junmo],
Tunnel Effect in CNNs: Image Reconstruction From Max Switch Locations,
SPLetters(24), No. 3, March 2017, pp. 254-258.
IEEE DOI 1702
Computer architecture. Reverse the neural network, reconstruct the image. BibRef

Donahue, J.[Jeff], Hendricks, L.A.[Lisa Anne], Rohrbach, M.[Marcus], Venugopalan, S.[Subhashini], Guadarrama, S.[Sergio], Saenko, K.[Kate], Darrell, T.J.[Trevor J.],
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,
PAMI(39), No. 4, April 2017, pp. 677-691.
IEEE DOI 1703
BibRef
Earlier: A1, A2, A5, A3, A4, A7, A6: CVPR15(2625-2634)
IEEE DOI 1510
Computational modeling BibRef

Hendricks, L.A.[Lisa Anne], Akata, Z.[Zeynep], Rohrbach, M.[Marcus], Donahue, J.[Jeff], Schiele, B.[Bernt], Darrell, T.J.[Trevor J.],
Generating Visual Explanations,
ECCV16(IV: 3-19).
Springer DOI 1611
Why the classification. BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei],
A novel companion objective function for regularization of deep convolutional neural networks,
IVC(60), No. 1, 2017, pp. 58-63.
Elsevier DOI 1704
BibRef
Earlier:
Regularization of deep neural networks using a novel companion objective function,
ICIP15(2865-2869)
IEEE DOI 1512
Convolutional neural network. Companion objective function BibRef

Sun, W.C.[Wei-Chen], Su, F.[Fei], Wang, L.Q.[Lei-Quan],
Improving deep neural networks with multilayer maxout networks,
VCIP14(334-337)
IEEE DOI 1504
filtering theory BibRef

Wang, L., Guo, S., Huang, W., Xiong, Y., Qiao, Y.,
Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs,
IP(26), No. 4, April 2017, pp. 2055-2068.
IEEE DOI 1704
Computer architecture BibRef

Wu, H.[Hao], Prasad, S.[Saurabh],
Convolutional Recurrent Neural Networks for Hyperspectral Data Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Ma, S.[Shugao], Bargal, S.A.[Sarah Adel], Zhang, J.M.[Jian-Ming], Sigal, L.[Leonid], Sclaroff, S.[Stan],
Do less and achieve more: Training CNNs for action recognition utilizing action images from the Web,
PR(68), No. 1, 2017, pp. 334-345.
Elsevier DOI 1704
Action recognition BibRef

Lu, J.[Jiwen], Liong, V.E.[Venice Erin], Zhou, J.[Jie],
Deep Hashing for Scalable Image Search,
IP(26), No. 5, May 2017, pp. 2352-2367.
IEEE DOI 1704
Binary codes. BibRef

Li, Y.[Yang], Xu, Y.L.[Yu-Long], Wang, J.B.[Jia-Bao], Miao, Z.[Zhuang], Zhang, Y.F.[Ya-Fei],
MS-RMAC: Multiscale Regional Maximum Activation of Convolutions for Image Retrieval,
SPLetters(24), No. 5, May 2017, pp. 609-613.
IEEE DOI 1704
feature extraction BibRef

Sun, B., Feng, H.,
Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach,
SPLetters(24), No. 6, June 2017, pp. 863-867.
IEEE DOI 1705
Compressed sensing, Cost function, Dictionaries, Sensors, Training, Wireless communication, Wireless sensor networks, Compressed sensing (CS), deep neural network, wireless neural recording BibRef

Xie, G.S., Zhang, X.Y., Yan, S., Liu, C.L.,
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation,
CirSysVideo(27), No. 6, June 2017, pp. 1263-1274.
IEEE DOI 1706
Convolutional codes, Databases, Dictionaries, Neural networks, Object oriented modeling, Training, Visualization, Convolutional neural networks (CNNs), Fisher vector, dictionary, domain adaptation (DA), part learning, scene, recognition BibRef

Zhou, W.X.[Wei-Xun], Newsam, S.[Shawn], Li, C.M.[Cong-Min], Shao, Z.F.[Zhen-Feng],
Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Fu, G.[Gang], Liu, C.J.[Chang-Jun], Zhou, R.[Rong], Sun, T.[Tao], Zhang, Q.[Qijian],
Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Zhao, W., Jiao, L., Ma, W., Zhao, J., Zhao, J., Liu, H., Cao, X., Yang, S.,
Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification,
GeoRS(55), No. 7, July 2017, pp. 4141-4156.
IEEE DOI 1706
Feature extraction, Image color analysis, Image segmentation, Machine learning, Neural networks, Remote sensing, Semantics, Convolution neural network (CNN), image classification, multiple local regions joint representation, panchromatic and multispectral (MS) images, superpixel-based BibRef

Ding, C.[Chen], Li, Y.[Ying], Xia, Y.[Yong], Wei, W.[Wei], Zhang, L.[Lei], Zhang, Y.N.[Yan-Ning],
Convolutional Neural Networks Based Hyperspectral Image Classification Method with Adaptive Kernels,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wu, F., Wang, Z., Lu, W., Li, X., Yang, Y., Luo, J., Zhuang, Y.,
Regularized Deep Belief Network for Image Attribute Detection,
CirSysVideo(27), No. 7, July 2017, pp. 1464-1477.
IEEE DOI 1707
Computational modeling, Context modeling, Correlation, Feature extraction, Neural networks, Semantics, Training, Contextual correlation, deep belief network (DBN), deep learning, image, attribute BibRef

Ioannidou, A.[Anastasia], Chatzilari, E.[Elisavet], Nikolopoulos, S.[Spiros], Kompatsiaris, I.[Ioannis],
Deep Learning Advances in Computer Vision with 3D Data: A Survey,
Surveys(50), No. 2, June 2017, pp. Article No 20.
DOI Link 1708
Survey, Deep Learning. This article surveys methods applying deep learning on 3D data and provides a classification based on how they exploit them. From the results of the examined works, we conclude that systems employing 2D views of 3D data typically surpass voxel-based (3D) deep models, which however, can perform better with more layers and severe data augmentation. Therefore, larger-scale datasets and increased resolutions are required. BibRef

Lu, W., Cheng, Y., Xiao, C., Chang, S., Huang, S., Liang, B., Huang, T.,
Unsupervised Sequential Outlier Detection With Deep Architectures,
IP(26), No. 9, September 2017, pp. 4321-4330.
IEEE DOI 1708
correlation methods, object detection, recurrent neural nets, applications domains, autoencoder models, critical systems, deep architectures, deep structured framework, feature construction, fine-tuning step, image analysis, layerwise training procedure, real-world benchmark data sets, recurrent neural networks, sequential anomaly detection, temporal correlation, time axis, unsupervised sequential outlier detection, video surveillance, Context, Context modeling, Correlation, Data models, Feature extraction, Principal component analysis, Training, Sequential anomaly detection, deep learning, denoising autoencoder, recurrent, neural, networks BibRef

Jin, K.H., McCann, M.T., Froustey, E., Unser, M.,
Deep Convolutional Neural Network for Inverse Problems in Imaging,
IP(26), No. 9, September 2017, pp. 4509-4522.
IEEE DOI 1708
computerised tomography, feedforward neural nets, image resolution, iterative methods, learning (artificial intelligence), medical image processing, CNN, GPU, adjoint operators, deep convolutional neural network, direct inversion, forward model, forward operators, hyperparameter selection, ill-posed inverse problems, image structure, multiresolution decomposition, normal-convolutional inverse problems, parallel beam X-ray computed tomography, regularized iterative algorithms, residual learning, synthetic phantoms, total variation-regularized iterative reconstruction, Computed tomography, Convolution, Image reconstruction, Inverse problems, Iterative methods, Neural networks, Image restoration, biomedical imaging, biomedical signal processing, computed tomography, image reconstruction, magnetic resonance imaging, reconstruction algorithms, tomography BibRef

Mei, S.H.[Shao-Hui], Ji, J.Y.[Jing-Yu], Hou, J.H.[Jun-Hui], Li, X.[Xu], Du, Q.[Qian],
Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks,
GeoRS(55), No. 8, August 2017, pp. 4520-4533.
IEEE DOI 1708
Feature extraction, Hyperspectral imaging, Image sensors, Machine learning, Principal component analysis, Sensors, convolutional neural network (CNN), feature learning, hyperspectral, spatial-spectral BibRef

Lee, H., Kwon, H.,
Going Deeper With Contextual CNN for Hyperspectral Image Classification,
IP(26), No. 10, October 2017, pp. 4843-4855.
IEEE DOI 1708
geophysical image processing, hyperspectral imaging, image classification, neural nets, CNN-based hyperspectral image classification, contextual deep CNN, joint spatio-spectral feature map, local contextual interactions, multiscale convolutional filter bank, Biological neural networks, Feature extraction, Hyperspectral imaging, Principal component analysis, Training, hyperspectral image classification, multi-scale filter bank, BibRef


Mao, H., Han, S., Pool, J., Li, W., Liu, X., Wang, Y., Dally, W.J.,
Exploring the Granularity of Sparsity in Convolutional Neural Networks,
Tensor17(1927-1934)
IEEE DOI 1709
Acceleration, Computational modeling, Grain size, Hardware, Kernel, Neural networks, Tensile stress BibRef

Dupre, R., Tzimiropoulos, G., Argyriou, V.,
Automated Risk Assessment for Scene Understanding and Domestic Robots Using RGB-D Data and 2.5D CNNs at a Patch Level,
DeepLearnRV17(476-477)
IEEE DOI 1709
Labeling, Machine learning, Shape, BibRef

Wang, J.B.[Jia-Bao], Li, Y.[Yang], Miao, Z.[Zhuang], Xu, Y.L.[Yu-Long], Tao, G.[Gang],
Euclidean output layer for discriminative feature extraction,
ICIVC17(150-153)
IEEE DOI 1708
Convolution, Face, Feature extraction, Neural networks, Testing, Training, Visualization, convolutional neural network, euclidean output layer, feature extraction, visual representation BibRef

Bentes Gatto, B.[Bernardo], de Souza, L.S.[Lincon Sales], dos Santos, E.M.[Eulanda M.],
A deep network model based on subspaces: A novel approach for image classification,
MVA17(436-439)
DOI Link 1708
Computer architecture, Discrete cosine transforms, Face, Face recognition, Machine learning, Neural networks, Principal component analysis BibRef

Hou, X.X.[Xian-Xu], Shen, L.L.[Lin-Lin], Sun, K.[Ke], Qiu, G.P.[Guo-Ping],
Deep Feature Consistent Variational Autoencoder,
WACV17(1133-1141)
IEEE DOI 1609
Correlation, Decoding, Face, Feature extraction, Image reconstruction, Loss measurement, Training BibRef

Wang, B., Yager, K., Yu, D., Hoai, M.,
X-Ray Scattering Image Classification Using Deep Learning,
WACV17(697-704)
IEEE DOI 1609
Feature extraction, Machine learning, Neural networks, Scattering, Training, X-ray imaging, X-ray, scattering BibRef

Zhao, J.P.[Jia-Ping], Itti, L.[Laurent],
Improved Deep Learning of Object Category Using Pose Information,
WACV17(550-559)
IEEE DOI 1609
Biological neural networks, Cameras, Convolution, Lighting, Neurons, Object recognition, Training BibRef

Zhao, J.P.[Jia-Ping], Chang, C.K., Itti, L.[Laurent],
Learning to Recognize Objects by Retaining Other Factors of Variation,
WACV17(560-568)
IEEE DOI 1609
Cameras, Computer architecture, Feature extraction, Image recognition, Lighting, Object recognition, Streaming, media BibRef

Mojoo, J.[Jonathan], Kurosawa, K.[Keiichi], Kurita, T.[Takio],
Deep CNN with Graph Laplacian Regularization for Multi-label Image Annotation,
ICIAR17(19-26).
Springer DOI 1706
BibRef

McCane, B., Szymanskic, L.,
Deep networks are efficient for circular manifolds,
ICPR16(3464-3469)
IEEE DOI 1705
Geometry, Logic gates, Manifolds, Neural networks, Neurons, Pattern recognition, Two, dimensional, displays BibRef

Shankar, T., Dwivedy, S.K., Guha, P.,
Reinforcement Learning via Recurrent Convolutional Neural Networks,
ICPR16(2592-2597)
IEEE DOI 1705
Belief propagation, Convolution, Learning (artificial intelligence), Mathematical model, Neural networks, Planning, Robots BibRef

Zhao, Z.B.[Zhen-Bing], Xu, G.Z.[Guo-Zhi], Qi, Y.C.[Yin-Cheng],
Multi-Scale Hierarchy Deep Feature Aggregation for Compact Image Representations,
DeepVisual16(III: 557-571).
Springer DOI 1704
BibRef

Li, M.M.[Ming-Ming], Ge, S.S.[Shuzhi Sam], Lee, T.H.[Tong Heng],
Glance and Glimpse Network: A Stochastic Attention Model Driven by Class Saliency,
DeepVisual16(III: 572-587).
Springer DOI 1704
Attention-based recurrent neural network (Glimpse Network) and a convolutional neural network (Glance Network). BibRef

Ke, T.W.[Tsung-Wei], Lin, C.W.[Che-Wei], Liu, T.L.[Tyng-Luh], Geiger, D.[Davi],
Variational Convolutional Networks for Human-Centric Annotations,
ACCV16(IV: 120-135).
Springer DOI 1704
BibRef

Krutsch, R., Naidu, S.,
Monte Carlo method based precision analysis of deep convolution nets,
DASIP16(162-167)
IEEE DOI 1704
Monte Carlo methods BibRef

Song, Y., Hong, X., McLoughlin, I., Dai, L.,
Image classification with CNN-based Fisher vector coding,
VCIP16(1-4)
IEEE DOI 1701
Computational modeling BibRef

Yan, S.,
Keynotes: Deep learning for visual understanding: Effectiveness vs. efficiency,
VCIP16(1-1)
IEEE DOI 1701
BibRef

Guo, J., Gould, S.,
Depth Dropout: Efficient Training of Residual Convolutional Neural Networks,
DICTA16(1-7)
IEEE DOI 1701
Biological neural networks BibRef

Vallet, A., Sakamoto, H.,
Convolutional Recurrent Neural Networks for Better Image Understanding,
DICTA16(1-7)
IEEE DOI 1701
Convolution BibRef

Yu, T.Y.[Tian-Yuan], Bai, L.[Liang], Guo, J.L.[Jin-Lin], Yang, Z.[Zheng], Xie, Y.X.[Yu-Xiang],
Deep Convolutional Neural Network for Bidirectional Image-Sentence Mapping,
MMMod17(II: 136-147).
Springer DOI 1701
BibRef

Islam, M.A.[M. Amirul], Bruce, N.[Neil], Wang, Y.[Yang],
Dense Image Labeling Using Deep Convolutional Neural Networks,
CRV16(16-23)
IEEE DOI 1612
Deep Convolutional Neural Network BibRef

Wang, B., Wang, L., Shuai, B., Zuo, Z., Liu, T., Chan, K.L., Wang, G.,
Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association,
DeepLearn-C16(386-393)
IEEE DOI 1612
BibRef

Zheng, S., Song, Y., Leung, T., Goodfellow, I.J.[Ian J.],
Improving the Robustness of Deep Neural Networks via Stability Training,
CVPR16(4480-4488)
IEEE DOI 1612
BibRef

Karianakis, N.[Nikolaos], Dong, J.M.[Jing-Ming], Soatto, S.[Stefano],
An Empirical Evaluation of Current Convolutional Architectures: Ability to Manage Nuisance Location and Scale Variability,
CVPR16(4442-4451)
IEEE DOI 1612
BibRef

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.,
Learning Deep Features for Discriminative Localization,
CVPR16(2921-2929)
IEEE DOI 1612
BibRef

Vijay Kumar, B.G., Carneiro, G.[Gustavo], Reid, I.D.[Ian D.],
Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimizing Global Loss Functions,
CVPR16(5385-5394)
IEEE DOI 1612
BibRef

Zhuang, B., Lin, G., Shen, C., Reid, I.D.,
Fast Training of Triplet-Based Deep Binary Embedding Networks,
CVPR16(5955-5964)
IEEE DOI 1612
BibRef

Lin, L., Wang, G., Zhang, R., Zhang, R., Liang, X., Zuo, W.,
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI 1612
BibRef

Herranz, L.[Luis], Jiang, S.Q.[Shu-Qiang], Li, X.,
Scene Recognition with CNNs: Objects, Scales and Dataset Bias,
CVPR16(571-579)
IEEE DOI 1612
BibRef

Yang, H.[Hao], Zhou, J.T.Y.[Joey Tian-Yi], Zhang, Y.[Yu], Gao, B.B.[Bin-Bin], Wu, J.X.[Jian-Xin], Cai, J.F.[Jian-Fei],
Exploit Bounding Box Annotations for Multi-Label Object Recognition,
CVPR16(280-288)
IEEE DOI 1612
BibRef

Xie, L.X.[Ling-Xi], Zheng, L.[Liang], Koller, O., Ney, H., Bowden, R.,
Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data is Continuous and Weakly Labelled,
CVPR16(3793-3802)
IEEE DOI 1612
BibRef

Noh, H., Seo, P.H., Han, B.,
Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction,
CVPR16(30-38)
IEEE DOI 1612
BibRef

Iandola, F.N., Moskewicz, M.W., Ashraf, K., Keutzer, K.,
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters,
CVPR16(2592-2600)
IEEE DOI 1612
BibRef

Murdock, C., Li, Z., Zhou, H., Duerig, T.,
Blockout: Dynamic Model Selection for Hierarchical Deep Networks,
CVPR16(2583-2591)
IEEE DOI 1612
BibRef

Long, G.[Gucan], Kneip, L.[Laurent], Alvarez, J.M.[Jose M.], Li, H.D.[Hong-Dong], Zhang, X.[Xiaohu], Yu, Q.[Qifeng],
Learning Image Matching by Simply Watching Video,
ECCV16(VI: 434-450).
Springer DOI 1611
BibRef

Huang, G.[Gao], Sun, Y.[Yu], Liu, Z.[Zhuang], Sedra, D.[Daniel], Weinberger, K.Q.[Kilian Q.],
Deep Networks with Stochastic Depth,
ECCV16(IV: 646-661).
Springer DOI 1611
BibRef

Li, Z.Z.[Zhi-Zhong], Hoiem, D.[Derek],
Learning Without Forgetting,
ECCV16(IV: 614-629).
Springer DOI 1611
Keep the old results in NN, but learn new capability. BibRef

Xie, S.N.[Sai-Ning], Huang, X.[Xun], Tu, Z.W.[Zhuo-Wen],
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior,
ECCV16(IV: 302-317).
Springer DOI 1611
BibRef

Do, T.T.[Thanh-Toan], Doan, A.D.[Anh-Dzung], Cheung, N.M.[Ngai-Man],
Learning to Hash with Binary Deep Neural Network,
ECCV16(V: 219-234).
Springer DOI 1611
BibRef

Rastegari, M.[Mohammad], Ordonez, V.[Vicente], Redmon, J.[Joseph], Farhadi, A.[Ali],
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks,
ECCV16(IV: 525-542).
Springer DOI 1611
BibRef

Gkioxari, G.[Georgia], Toshev, A.[Alexander], Jaitly, N.[Navdeep],
Chained Predictions Using Convolutional Neural Networks,
ECCV16(IV: 728-743).
Springer DOI 1611
BibRef

Dai, J.[Jifeng], He, K.M.[Kai-Ming], Li, Y.[Yi], Ren, S.Q.[Shao-Qing], Sun, J.[Jian],
Instance-Sensitive Fully Convolutional Networks,
ECCV16(VI: 534-549).
Springer DOI 1611
BibRef

Kalantidis, Y.[Yannis], Mellina, C.[Clayton], Osindero, S.[Simon],
Cross-Dimensional Weighting for Aggregated Deep Convolutional Features,
WebScale16(I: 685-701).
Springer DOI 1611
BibRef

Sun, Z.[Zhun], Ozay, M.[Mete], Okatani, T.[Takayuki],
Design of Kernels in Convolutional Neural Networks for Image Classification,
ECCV16(VII: 51-66).
Springer DOI 1611
BibRef

Roy, A.[Anirban], Todorovic, S.[Sinisa],
A Multi-scale CNN for Affordance Segmentation in RGB Images,
ECCV16(IV: 186-201).
Springer DOI 1611
BibRef

Walach, E.[Elad], Wolf, L.B.[Lior B.],
Learning to Count with CNN Boosting,
ECCV16(II: 660-676).
Springer DOI 1611
BibRef

Jackson, A.S.[Aaron S.], Valstar, M.[Michel], Tzimiropoulos, G.[Georgios],
A CNN Cascade for Landmark Guided Semantic Part Segmentation,
DeepLearn16(III: 143-155).
Springer DOI 1611
BibRef

Hinami, R.[Ryota], Satoh, S.[Shin'ichi],
Large-Scale R-CNN with Classifier Adaptive Quantization,
ECCV16(III: 403-419).
Springer DOI 1611
BibRef

Sajjadi, M., Javanmardi, M., Tasdizen, T.,
Mutual exclusivity loss for semi-supervised deep learning,
ICIP16(1908-1912)
IEEE DOI 1610
Entropy BibRef

Papadopoulos, G.T.[Georgios T.], Machairidou, E.[Elpida], Daras, P.[Petros],
Deep cross-layer activation features for visual recognition,
ICIP16(923-927)
IEEE DOI 1610
Correlation. Last layer of the CNN may not capture every scale of feature. BibRef

Qi, M.S.[Meng-Shi], Wang, Y.H.[Yun-Hong],
DEEP-CSSR: Scene classification using category-specific salient region with deep features,
ICIP16(1047-1051)
IEEE DOI 1610
Bio inspired models. BibRef

Yang, Y.[Yi], Chen, F.[Feng], Chen, X.M.[Xiao-Ming], Dai, Y.[Yan], Chen, Z.Y.[Zhen-Yang], Ji, J.[Jiang], Zhao, T.[Tong],
Video system for human attribute analysis using compact convolutional neural network,
ICIP16(584-588)
IEEE DOI 1610
Data models BibRef

Anantrasirichai, N., Gilchrist, I.D., Bull, D.R.,
Visual salience and priority estimation for locomotion using a deep convolutional neural network,
ICIP16(1599-1603)
IEEE DOI 1610
Estimation BibRef

Ahmadi, A., Patras, I.,
Unsupervised convolutional neural networks for motion estimation,
ICIP16(1629-1633)
IEEE DOI 1610
Adaptive optics BibRef

Mayhew, M.B., Chen, B., Ni, K.S.,
Assessing semantic information in convolutional neural network representations of images via image annotation,
ICIP16(2266-2270)
IEEE DOI 1610
Feature extraction BibRef

Venkatesan, R.[Ragav], Gatupalli, V.[Vijetha], Li, B.X.[Bao-Xin],
On the generality of neural image features,
ICIP16(41-45)
IEEE DOI 1610
Filters learned by CNNs. Atomic layer deposition BibRef

Zhou, D., Li, X., Zhang, Y.J.,
A novel CNN-based match kernel for image retrieval,
ICIP16(2445-2449)
IEEE DOI 1610
Correlation BibRef

Blot, M., Cord, M., Thome, N.,
Max-min convolutional neural networks for image classification,
ICIP16(3678-3682)
IEEE DOI 1610
Computer architecture BibRef

Chaabouni, S., Benois-Pineau, J., Ben Amar, C.,
Transfer learning with deep networks for saliency prediction in natural video,
ICIP16(1604-1608)
IEEE DOI 1610
Benchmark testing BibRef

Makantasis, K., Doulamis, A., Doulamis, N., Psychas, K.,
Deep learning based human behavior recognition in industrial workflows,
ICIP16(1609-1613)
IEEE DOI 1610
Computer architecture BibRef

Gaur, U., Kourakis, M., Newman-Smith, E., Smith, W., Manjunath, B.S.,
Membrane segmentation via active learning with deep networks,
ICIP16(1943-1947)
IEEE DOI 1610
Computer architecture BibRef

Liu, M.Y.[Ming-Yu], Mallya, A.[Arun], Tuzel, O.[Oncel], Chen, X.[Xi],
Unsupervised network pretraining via encoding human design,
WACV16(1-9)
IEEE DOI 1606
Computer architecture. Deep NN training. BibRef

Porter, R.B., Zimmer, B.G.,
Deep segmentation networks using 'simple' multi-layered graphical models,
Southwest16(41-44)
IEEE DOI 1605
Feeds BibRef

Karmakar, P., Teng, S.W., Zhang, D., Liu, Y., Lu, G.,
Combining Pyramid Match Kernel and Spatial Pyramid for Image Classification,
DICTA16(1-8)
IEEE DOI 1701
Databases BibRef

Karmakar, P., Teng, S.W., Lu, G., Zhang, D.,
Rotation Invariant Spatial Pyramid Matching for Image Classification,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Hiranandani, G., Karnick, H.,
Improved Classification and Reconstruction by Introducing Independence and Randomization in Deep Neural Networks,
DICTA15(1-8)
IEEE DOI 1603
image classification BibRef

Ueki, K., Kobayashi, T.,
Multi-layer feature extractions for image classification: Knowledge from deep CNNs,
WSSIP15(9-12)
IEEE DOI 1603
BibRef
And: WSSIP15(9-12)
IEEE DOI 1603
feature extraction. Computer vision 2 papers listed. BibRef

Ba, J.L.[Jimmy Lei], Swersky, K.[Kevin], Fidler, S.[Sanja], Salakhutdinov, R.[Ruslan],
Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions,
ICCV15(4247-4255)
IEEE DOI 1602
Electronic publishing BibRef

Huang, J.J.[Jia-Ji], Qiu, Q.[Qiang], Calderbank, R.[Robert], Sapiro, G.[Guillermo],
Geometry-Aware Deep Transform,
ICCV15(4139-4147)
IEEE DOI 1602
Machine learning. Use geometry. BibRef

Goroshin, R.[Ross], Bruna, J.[Joan], Tompson, J.[Jonathan], Eigen, D.[David], Le Cun, Y.L.[Yann L.],
Unsupervised Learning of Spatiotemporally Coherent Metrics,
ICCV15(4086-4093)
IEEE DOI 1602
Convolution BibRef

Aubry, M.[Mathieu], Russell, B.C.[Bryan C.],
Understanding Deep Features with Computer-Generated Imagery,
ICCV15(2875-2883)
IEEE DOI 1602
Computational modeling BibRef

Wang, X.L.[Xiao-Long], Gupta, A.[Abhinav],
Unsupervised Learning of Visual Representations Using Videos,
ICCV15(2794-2802)
IEEE DOI 1602
Clustering algorithms. Unsupervised. BibRef

Arnab, A.[Anurag], Jayasumana, S.[Sadeep], Zheng, S.[Shuai], Torr, P.H.S.[Philip H. S.],
Higher Order Conditional Random Fields in Deep Neural Networks,
ECCV16(II: 524-540).
Springer DOI 1611
BibRef

Zheng, S.[Shuai], Jayasumana, S.[Sadeep], Romera-Paredes, B.[Bernardino], Vineet, V.[Vibhav], Su, Z.Z.[Zhi-Zhong], Du, D.L.[Da-Long], Huang, C.[Chang], Torr, P.H.S.[Philip H. S.],
Conditional Random Fields as Recurrent Neural Networks,
ICCV15(1529-1537)
IEEE DOI 1602
Combine CNN with CRF. BibRef

Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A., Chang, S.F.,
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections,
ICCV15(2857-2865)
IEEE DOI 1602
Complexity theory BibRef

Feng, J., Darrell, T.J.,
Learning the Structure of Deep Convolutional Networks,
ICCV15(2749-2757)
IEEE DOI 1602
Adaptation models BibRef

Wu, R., Wang, B., Wang, W., Yu, Y.,
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification,
ICCV15(1287-1295)
IEEE DOI 1602
Aggregates BibRef

Yan, Z., Zhang, H., Piramuthu, R., Jagadeesh, V., De Coste, D., Di, W., Yu, Y.,
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition,
ICCV15(2740-2748)
IEEE DOI 1602
Computer architecture BibRef

Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.,
Discriminative Learning of Deep Convolutional Feature Point Descriptors,
ICCV15(118-126)
IEEE DOI 1602
Computational modeling BibRef

Sigurdsson, G.A.[Gunnar A.], Chen, X.L.[Xin-Lei], Gupta, A.[Abhinav],
Learning Visual Storylines with Skipping Recurrent Neural Networks,
ECCV16(V: 71-88).
Springer DOI 1611
BibRef
And: A2, A3, Only:
Webly Supervised Learning of Convolutional Networks,
ICCV15(1431-1439)
IEEE DOI 1602
Data models BibRef

Masci, J., Boscaini, D., Bronstein, M.M., Vandergheynst, P.,
Geodesic Convolutional Neural Networks on Riemannian Manifolds,
3DRR15(832-840)
IEEE DOI 1602
Eigenvalues and eigenfunctions BibRef

Gordo, A.[Albert], Gaidon, A.[Adrien], Perronnin, F.[Florent],
Deep Fishing: Gradient Features from Deep Nets,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Zha, S.X.[Sheng-Xin], Luisier, F.[Florian], Andrews, W.[Walter], Srivastava, N.[Nitish], Salakhutdinov, R.[Ruslan],
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Raj, A.[Anant], Namboodiri, V.P.[Vinay P.], Tuytelaars, T.[Tinne],
Subspace Alignment Based Domain Adaptation for RCNN Detector,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Graham, B.[Ben],
Sparse 3D convolutional neural networks,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Bilen, H., Vedaldi, A.,
Weakly Supervised Deep Detection Networks,
CVPR16(2846-2854)
IEEE DOI 1612
BibRef

Lenc, K.[Karel], Vedaldi, A.[Andrea],
R-CNN minus R,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yang, M.[Mu], Li, B.[Brian], Fan, H.Q.[Hao-Qiang], Jiang, Y.N.[Yu-Ning],
Randomized spatial pooling in deep convolutional networks for scene recognition,
ICIP15(402-406)
IEEE DOI 1512
deep convolutional networks BibRef

Nguyen, K.[Kien], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Deep Context Modeling for Semantic Segmentation,
WACV17(56-63)
IEEE DOI 1609
BibRef
Earlier:
Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling,
ICIP16(1344-1348)
IEEE DOI 1610
BibRef
Earlier:
Improving deep convolutional neural networks with unsupervised feature learning,
ICIP15(2270-2274)
IEEE DOI 1512
Feature extraction, Graphical models, Image segmentation, Kernel, Labeling, Neural networks, Semantics, context modeling, scene parsing, scene understanding, semantic segmentation. Computational modeling. Convolutional Neural Network BibRef

Kang, S.H.[Sung-Hun], Lee, D.H.[Dong-Hoon], Yoo, C.D.[Chang D.],
Face attribute classification using attribute-aware correlation map and gated convolutional neural networks,
ICIP15(4922-4926)
IEEE DOI 1512
Attribute classification BibRef

Peng, K.C.[Kuan-Chuan], Chen, T.H.[Tsu-Han],
Toward correlating and solving abstract tasks using convolutional neural networks,
WACV16(1-9)
IEEE DOI 1606
BibRef
Earlier:
Cross-layer features in convolutional neural networks for generic classification tasks,
ICIP15(3057-3061)
IEEE DOI 1512
Convolutional neural networks (CNN) BibRef

Alam, M.M.[M. Mushfiqul], Patil, P.[Pranita], Hagan, M.T.[Martin T.], Chandler, D.M.[Damon M.],
A computational model for predicting local distortion visibility via convolutional neural network trained on natural scenes,
ICIP15(3967-3971)
IEEE DOI 1512
Local distortion visibility BibRef

Talathi, S.S.[Sachin S.],
Hyper-parameter optimization of deep convolutional networks for object recognition,
ICIP15(3982-3986)
IEEE DOI 1512
deep convolution networks BibRef

Yamashita, T.[Takayoshi], Tanaka, M.[Masayuki], Yamauchi, Y.[Yuji], Fujiyoshi, H.[Hironobu],
SWAP-NODE: A regularization approach for deep convolutional neural networks,
ICIP15(2475-2479)
IEEE DOI 1512
deep learning; dropout; regularization; swap-node BibRef

Afzal, M.Z.[Muhammad Zeshan], Capobianco, S.[Samuele], Malik, M.I.[Muhammad Imran], Marinai, S.[Simone], Breuel, T.M.[Thomas M.], Dengel, A.[Andreas], Liwicki, M.[Marcus],
Deepdocclassifier: Document classification with deep Convolutional Neural Network,
ICDAR15(1111-1115)
IEEE DOI 1511
Convolutional Neural Network;Deep CNN;Document Image Classification BibRef

Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
A Convnet for Non-maximum Suppression,
GCPR16(192-204).
Springer DOI 1611
BibRef

Christodoulidis, S.[Stergios], Anthimopoulos, M.[Marios], Mougiakakou, S.[Stavroula],
Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks,
MADiMa15(458-465).
Springer DOI 1511
BibRef

Rosa, G.[Gustavo], Papa, J.[João], Marana, A.[Aparecido], Scheirer, W.[Walter], Cox, D.[David],
Fine-Tuning Convolutional Neural Networks Using Harmony Search,
CIARP15(683-690).
Springer DOI 1511
BibRef

Bhalla, V.[Vandna], Chaudhury, S.[Santanu], Jain, A.[Arihant],
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine,
PReMI15(215-224).
Springer DOI 1511
BibRef

Li, Y.[Yao], Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Mid-level deep pattern mining,
CVPR15(971-980)
IEEE DOI 1510
Convolutional Neural Networks. BibRef

Escorcia, V.[Victor], Niebles, J.C.[Juan Carlos], Ghanem, B.[Bernard],
On the relationship between visual attributes and convolutional networks,
CVPR15(1256-1264)
IEEE DOI 1510
BibRef

He, K.M.[Kai-Ming], Sun, J.[Jian],
Convolutional neural networks at constrained time cost,
CVPR15(5353-5360)
IEEE DOI 1510
BibRef

Liu, B.Y.[Bao-Yuan], Wang, M.[Min], Foroosh, H.[Hassan], Tappen, M.[Marshall], Penksy, M.[Marianna],
Sparse Convolutional Neural Networks,
CVPR15(806-814)
IEEE DOI 1510
BibRef

Liang, M.[Ming], Hu, X.L.[Xiao-Lin],
Recurrent convolutional neural network for object recognition,
CVPR15(3367-3375)
IEEE DOI 1510
BibRef

Zagoruyko, S.[Sergey], Komodakis, N.[Nikos],
Learning to compare image patches via convolutional neural networks,
CVPR15(4353-4361)
IEEE DOI 1510
BibRef

Yoo, D.G.[Dong-Geun], Park, S.G.[Sung-Gyun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Multi-scale pyramid pooling for deep convolutional representation,
DeepLearn15(71-80)
IEEE DOI 1510
Accuracy BibRef

Paisitkriangkrai, S.[Sakrapee], Sherrah, J.[Jamie], Janney, P.[Pranam], van den Hengel, A.J.[Anton J.],
Effective semantic pixel labelling with convolutional networks and Conditional Random Fields,
EarthObserv15(36-43)
IEEE DOI 1510
Accuracy BibRef

Workman, S.[Scott], Jacobs, N.[Nathan],
On the location dependence of convolutional neural network features,
EarthObserv15(70-78)
IEEE DOI 1510
Databases BibRef

Liu, L.Q.[Ling-Qiao], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification,
CVPR15(4749-4757)
IEEE DOI 1510
DCNN. BibRef

Jie, Z.[Zequn], Yan, S.C.[Shui-Cheng],
Robust Scene Classification with Cross-Level LLC Coding on CNN Features,
ACCV14(II: 376-390).
Springer DOI 1504
CNN: Convolutional Neural Network. LLC: locality-constrained linear coding. BibRef

Simon, M.[Marcel], Rodner, E.[Erik],
Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks,
ICCV15(1143-1151)
IEEE DOI 1602
Birds BibRef

Denzler, J.[Joachim], Rodner, E.[Erik], Simon, M.[Marcel],
Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception,
CVAA16(I: 871-887).
Springer DOI 1611
BibRef

Simon, M.[Marcel], Rodner, E.[Erik], Denzler, J.[Joachim],
Part Detector Discovery in Deep Convolutional Neural Networks,
ACCV14(II: 162-177).
Springer DOI 1504
BibRef

Ng, J.Y.H.[Joe Yue-Hei], Hausknecht, M.[Matthew], Vijayanarasimhan, S.[Sudheendra], Vinyals, O.[Oriol], Monga, R.[Rajat], Toderici, G.[George],
Beyond short snippets: Deep networks for video classification,
CVPR15(4694-4702)
IEEE DOI 1510
BibRef

Ozeki, M.[Makoto], Okatani, T.[Takayuki],
Understanding Convolutional Neural Networks in Terms of Category-Level Attributes,
ACCV14(II: 362-375).
Springer DOI 1504
BibRef

Maire, F., Mejias, L., Hodgson, A.,
A Convolutional Neural Network for Automatic Analysis of Aerial Imagery,
DICTA14(1-8)
IEEE DOI 1502
entropy BibRef

Chen, G.B.[Guo-Bin], Han, T.X.[Tony X.], He, Z.[Zhihai], Kays, R.[Roland], Forrester, T.[Tavis],
Deep convolutional neural network based species recognition for wild animal monitoring,
ICIP14(858-862)
IEEE DOI 1502
Birds BibRef

Hafemann, L.G.[Luiz G.], Oliveira, L.S.[Luiz S.], Cavalin, P.[Paulo],
Forest Species Recognition Using Deep Convolutional Neural Networks,
ICPR14(1103-1107)
IEEE DOI 1412
Accuracy BibRef

Frazão, X.[Xavier], Alexandre, L.A.[Luís A.],
Weighted Convolutional Neural Network Ensemble,
CIARP14(674-681).
Springer DOI 1411
BibRef
And:
DropAll: Generalization of Two Convolutional Neural Network Regularization Methods,
ICIAR14(I: 282-289).
Springer DOI 1410
BibRef

Gatta, C.[Carlo], Romero, A.[Adriana], van de Veijer, J.[Joost],
Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep Networks,
DeepLearn14(504-511)
IEEE DOI 1409
BibRef

Gong, Y.C.[Yun-Chao], Wang, L.W.[Li-Wei], Guo, R.Q.[Rui-Qi], Lazebnik, S.[Svetlana],
Multi-scale Orderless Pooling of Deep Convolutional Activation Features,
ECCV14(VII: 392-407).
Springer DOI 1408
Deep convolutional neural networks BibRef

Oquab, M.[Maxime], Bottou, L.[Leon], Laptev, I.[Ivan], Sivic, J.[Josef],
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks,
CVPR14(1717-1724)
IEEE DOI 1409
BibRef

Yin, X.C.[Xu-Cheng], Yang, C.[Chun], Pei, W.Y.[Wei-Yi], Hao, H.W.[Hong-Wei],
Shallow Classification or Deep Learning: An Experimental Study,
ICPR14(1904-1909)
IEEE DOI 1412
Character recognition BibRef

Kekec, T.[Taygun], Emonet, R.[Remi], Fromont, E.[Elisa], Tremeau, A.[Alain], Wolf, C.[Christian],
Contextually Constrained Deep Networks for Scene Labeling,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Zhong, S.H.[Sheng-Hua], Liu, Y.[Yan], Chung, F.L.[Fu-Lai], Wu, G.S.[Gang-Shan],
Semiconducting bilinear deep learning for incomplete image recognition,
ICMR12(32).
DOI Link 1301
semiconducting bilinear deep belief networks (SBDBN) human's visual cortex. BibRef

Ciregan, D.[Dan], Meier, U.[Ueli], Schmidhuber, J.[Jurgen],
Multi-column deep neural networks for image classification,
CVPR12(3642-3649).
IEEE DOI 1208
BibRef

Simard, P.Y., Steinkraus, D., Platt, J.C.,
Best practices for convolutional neural networks applied to visual document analysis,
ICDAR03(958-963).
IEEE DOI 0311
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks Implementation Issues .


Last update:Sep 18, 2017 at 11:34:11