14.5.7.5 Convolutional Neural Networks for Image Descriptions

Chapter Contents (Back)
Convolutional Neural Networks. CNN. Neural Networks. CNN for Image Descriptions. Implementation issues: See also Convolutional Neural Networks, Design, Implementation Issues. See also Graph Convolutional Neural Networks. See also Deep Learning, Deep Nets. See also Adversarial Networks, Adversarial Inputs, Generative Adversarial. See also Recurrent Neural Networks for Shapes and Complex Features, RNN. ResNets: See also Residual Neural Networks, ResNet. See also Efficient Implementations Convolutional Neural Networks. See also Data Augmentation, Generative Network, Convolutional Network.

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Paulin, M.[Mattis], Mairal, J.[Julien], Douze, M.[Matthijs], Harchaoui, Z.[Zaid], Perronnin, F.[Florent], Schmid, C.[Cordelia],
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IJCV(121), No. 1, January 2017, pp. 149-168.
Springer DOI 1702
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Earlier: A1, A3, A4, A2, A5, A6:
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ICCV15(91-99)
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Wang, Z.[Zhe], Wang, L.M.[Li-Min], Wang, Y.L.[Ya-Li], Zhang, B.[Bowen], Qiao, Y.[Yu],
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IEEE DOI 1702
Computer architecture. Reverse the neural network, reconstruct the image. BibRef

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Li, Y.[Yang], Xu, Y.L.[Yu-Long], Wang, J.B.[Jia-Bao], Miao, Z.[Zhuang], Zhang, Y.F.[Ya-Fei],
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IEEE DOI 1704
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Xie, G.S., Zhang, X.Y., Yan, S., Liu, C.L.,
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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],
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Fu, G.[Gang], Liu, C.J.[Chang-Jun], Zhou, R.[Rong], Sun, T.[Tao], Zhang, Q.[Qijian],
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Zhao, W., Jiao, L., Ma, W., Zhao, J., Zhao, J., Liu, H., Cao, X., Yang, S.,
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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

Mei, S.H.[Shao-Hui], Ji, J.Y.[Jing-Yu], Hou, J.H.[Jun-Hui], Li, X.[Xu], Du, Q.[Qian],
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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

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Classification, convolutional long short-term memory (ConvLSTM), deep learning, hyperspectral image (HSI) BibRef

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IEEE DOI 1710
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Yang, Z.S.[Zhi-Shuang], Jiang, W.S.[Wan-Shou], Xu, B.[Bo], Zhu, Q.S.[Quan-Sheng], Jiang, S.[San], Huang, W.[Wei],
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PandRS(140), 2018, pp. 133-144.
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Convolutional neural network, Multilayer perceptron, VFSR remotely sensed imagery, Fusion decision, Feature representation BibRef

Guo, Y.M.[Yan-Ming], Liu, Y.[Yu], Lao, S.Y.[Song-Yang], Bakker, E.M.[Erwin M.], Bai, L.[Liang], Lew, M.S.[Michael S.],
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MultMed(20), No. 6, June 2018, pp. 1525-1536.
IEEE DOI 1805
Computer architecture, Convolutional codes, Feature extraction, Neural networks, Semantics, Visualization, visual recognition BibRef

Pan, X.[Xin], Zhao, J.[Jian],
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Hao, Y., Li, Q., Mo, H., Zhang, H., Li, H.,
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SPLetters(25), No. 7, July 2018, pp. 1064-1068.
IEEE DOI 1807
affine transforms, computer vision, convolution, feature extraction, feedforward neural nets, transformation BibRef

Zhang, J.P.[Jin-Peng], Zhang, J.M.[Jin-Ming],
An Analysis of CNN Feature Extractor Based on KL Divergence,
IJIG(18), No. 3, July 2018, pp. Article 1850017.
DOI Link 1807
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Yang, H., Chen, T., Tu, C., Chen, C.,
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SPLetters(25), No. 10, October 2018, pp. 1590-1594.
IEEE DOI 1810
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Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.,
Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification,
GeoRS(56), No. 11, November 2018, pp. 6712-6722.
IEEE DOI 1811
Feature extraction, Measurement, Support vector machines, Training, Machine learning, Semantics, Hyperspectral imaging, spectral-spatial feature BibRef

Chen, T.[Tao], Lu, S.J.[Shi-Jian], Fan, J.Y.[Jia-Yuan],
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IEEE DOI 1903
convolutional neural nets, feature extraction, image annotation, image classification, learning (artificial intelligence), image classification BibRef

Yang, X.F.[Xiao-Fei], Zhang, X.F.[Xiao-Feng], Ye, Y.M.[Yun-Ming], Lau, R.Y.K.[Raymond Y. K.], Lu, S.J.[Shi-Jian], Li, X.T.[Xu-Tao], Huang, X.H.[Xiao-Hui],
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IEEE DOI 1904
Feature extraction, Dynamics, Vehicle dynamics, Task analysis, Convolutional neural networks, long term frequency feature BibRef

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GeoRS(57), No. 5, May 2019, pp. 2494-2509.
IEEE DOI 1905
convolutional neural nets, entropy, geophysical image processing, image classification, interclass relationship BibRef

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Elsevier DOI 1906
Variational auto-encoder, Regularization, Knowledge representation, Perceptual data compaction, Statistical performance analysis BibRef

Gong, Z., Zhong, P., Yu, Y., Hu, W., Li, S.,
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GeoRS(57), No. 6, June 2019, pp. 3599-3618.
IEEE DOI 1906
Hyperspectral imaging, Measurement, Feature extraction, Training, Convolution, Task analysis, Convolutional neural network (CNN), multiscale features BibRef

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IEEE DOI 1908
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IEEE DOI 1908
convolutional neural nets, geophysical image processing, hyperspectral imaging, image classification, transfer learning BibRef

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Xie, J.[Jie], He, N.J.[Nan-Jun], Fang, L.Y.[Le-Yuan], Plaza, A.J.[Antonio J.],
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IEEE DOI 1909
Remote sensing, Feature extraction, Data models, Image color analysis, Kernel, Semantics, remote sensing scene classification BibRef

Guo, A.J.X., Zhu, F.,
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GeoRS(57), No. 9, September 2019, pp. 7170-7181.
IEEE DOI 1909
Feature extraction, Training, Hyperspectral imaging, Testing, Training data, Adaptation models, Convolutional neural networks, hyperspectral image classification BibRef

Zhang, M., Li, W., Du, Q., Gao, L., Zhang, B.,
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IEEE DOI 1910
Feature extraction, Laser radar, Decoding, Computer architecture, Hyperspectral imaging, Task analysis, multisensor fusion BibRef

Lu, X., Sun, H., Zheng, X.,
A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification,
GeoRS(57), No. 10, October 2019, pp. 7894-7906.
IEEE DOI 1910
convolutional neural nets, feature extraction, geophysical image processing, image classification, scene classification BibRef

Lu, X., Gong, T., Zheng, X.,
Multisource Compensation Network for Remote Sensing Cross-Domain Scene Classification,
GeoRS(58), No. 4, April 2020, pp. 2504-2515.
IEEE DOI 2004
Remote sensing, Feature extraction, Training, Neural networks, Task analysis, Sensors, Optics, Cross-domain scene classification, remote sensing scene classification BibRef

Sun, H., Li, S., Zheng, X., Lu, X.,
Remote Sensing Scene Classification by Gated Bidirectional Network,
GeoRS(58), No. 1, January 2020, pp. 82-96.
IEEE DOI 2001
Feature extraction, Nonhomogeneous media, Logic gates, Aggregates, Encoding, Interference, Task analysis, Feature aggregation, scene classification BibRef

Santa Cruz, R.[Rodrigo], Fernando, B.[Basura], Cherian, A.[Anoop], Gould, S.[Stephen],
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PAMI(41), No. 12, December 2019, pp. 3100-3114.
IEEE DOI 1911
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Moradi, R.[Reza], Berangi, R.[Reza], Minaei, B.[Behrooz],
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Elsevier DOI 2001
Feature combination, Network architecture, Selective feature connection mechanism, Convolutional neural network BibRef

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PAMI(41), No. 11, November 2019, pp. 2553-2567.
IEEE DOI 1910
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IEEE DOI 1711
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IEEE DOI 2003
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IEEE DOI 1910
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Li, X.[Xian], Ding, M.L.[Ming-Li], Pižurica, A.[Aleksandra],
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IEEE DOI 2004
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IEEE DOI 1910
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Xu, H.Y.[Hong-Yu], Wang, Z.Y.[Zhang-Yang], Yang, H.C.[Hai-Chuan], Liu, D.[Ding], Liu, J.[Ji],
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Encoding, Dictionaries, Machine learning, Standards, Time complexity, Task analysis, Sparse representation, feature learning, unsupervised learning BibRef

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Image retrieval, In-depth learning, Feature extraction, Convolutional neural network BibRef

Furuta, R.[Ryosuke], Inoue, N.[Naoto], Yamasaki, T.[Toshihiko],
PixelRL: Fully Convolutional Network With Reinforcement Learning for Image Processing,
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Image color analysis, Task analysis, Image denoising, Learning systems, Image restoration, Convolution, saliency-driven image editing BibRef


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Towards Learning Affine-Invariant Representations via Data-Efficient CNNs,
WACV20(893-902)
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Shen, Y.M.[Yu-Ming], Qin, J.[Jie], Chen, J.X.[Jia-Xin], Liu, L.[Li], Zhu, F.[Fan], Shen, Z.Y.[Zi-Yi],
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Prakash, A.[Aaditya], Storer, J.[James], Florencio, D.[Dinei], Zhang, C.[Cha],
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Zhu, X.Z.[Xi-Zhou], Hu, H.[Han], Lin, S.[Stephen], Dai, J.F.[Ji-Feng],
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Zhang, J.J.[Jun-Jian], Li, C.G.[Chun-Guang], You, C.[Chong], Qi, X.B.[Xian-Biao], Zhang, H.G.[Hong-Gang], Guo, J.[Jun], Lin, Z.C.[Zhou-Chen],
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IEEE DOI 2002
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Shao, W.Q.[Wen-Qi], Meng, T.J.[Tian-Jian], Li, J.Y.[Jing-Yu], Zhang, R.M.[Rui-Mao], Li, Y.[Yudian], Wang, X.G.[Xiao-Gang], Luo, P.[Ping],
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Hein, M.[Matthias], Andriushchenko, M.[Maksym], Bitterwolf, J.[Julian],
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Sinha, P., Psaromiligkos, I., Zilic, Z.,
A Structurally Regularized Convolutional Neural Network for Image Classification Using Wavelet-Based Subband Decomposition,
ICIP19(649-653)
IEEE DOI 1910
CNN, wavelet-based subband decomposition, image classification, regularization BibRef

Devaram, R.R.[Rami Reddy], Allegra, D.[Dario], Gallo, G.[Giovanni], Stanco, F.[Filippo],
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Mendolia, I.[Isabella], Contino, S.[Salvatore], Perricone, U.[Ugo], Pirrone, R.[Roberto], Ardizzone, E.[Edoardo],
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Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], Laaksonen, J.[Jorma], Zaki, N.[Nazar],
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BibRef

Yang, S.[Shiqi], Peng, G.[Gang],
Parallel Convolutional Networks for Image Recognition via a Discriminator,
ACCV18(I:609-624).
Springer DOI 1906
BibRef

Dharmasiri, T.[Thanuja], Spek, A.[Andrew], Drummond, T.W.[Tom W.],
ENG: End-to-End Neural Geometry for Robust Depth and Pose Estimation Using CNNs,
ACCV18(I:625-642).
Springer DOI 1906
BibRef

Gudovskiy, D.A.[Denis A.], Hodgkinson, A.[Alec], Rigazio, L.[Luca],
DNN Feature Map Compression Using Learned Representation over GF(2),
CEFR-LCV18(IV:502-516).
Springer DOI 1905
BibRef

Hansen, L.[Lasse], Diesel, J.[Jasper], Heinrich, M.P.[Mattias P.],
Multi-kernel Diffusion CNNs for Graph-Based Learning on Point Clouds,
DeepLearn-G18(III:456-469).
Springer DOI 1905
BibRef

Wang, M., Zhou, J., Mao, W., Gong, M.,
Multi-Scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets,
WACV19(321-330)
IEEE DOI 1904
computer vision, convolutional neural nets, feature extraction, image classification, learning (artificial intelligence), Computer architecture BibRef

Qiu, S.[Suo], Xu, X.M.[Xiang-Min], Cai, B.[Bolun],
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks,
ICPR18(1223-1228)
IEEE DOI 1812
Training, Convolutional neural networks, Convergence, Standards, Backpropagation, Task analysis BibRef

Liu, Y.T.[Yun-Tao], Dou, Y.[Yong], Jin, R.C.[Ruo-Chun], Qiao, P.[Peng],
Visual Tree Convolutional Neural Network in Image Classification,
ICPR18(758-763)
IEEE DOI 1812
Identify confused categories. Visualization, Task analysis, Dogs, Training, Vegetation, Predictive models, Detection algorithms BibRef

Xu, X.[Xin], Wang, W.[Wei],
Target Group Distribution Pattern Discovery via Convolutional Neural Network,
ICPR18(266-271)
IEEE DOI 1812
Pattern analysis, Shape, Convolutional neural networks, Bagging, Unmanned aerial vehicles, BibRef

Yang, H., Zhang, X., Yin, F., Liu, C.,
Robust Classification with Convolutional Prototype Learning,
CVPR18(3474-3482)
IEEE DOI 1812
Prototypes, Feature extraction, Robustness, Task analysis, Training, Pattern recognition, Convolutional neural networks BibRef

Liu, W.Y.[Wei-Yang], Liu, Z.[Zhen], Yu, Z.D.[Zhi-Ding], Dai, B.[Bo], Lin, R.M.[Rong-Mei], Wang, Y.S.[Yi-Sen], Rehg, J.M.[James M.], Song, L.[Le],
Decoupled Networks,
CVPR18(2771-2779)
IEEE DOI 1812
Convolution, Semantics, Kernel, Convergence, Task analysis, Robustness, Convolutional neural networks BibRef

Zhu, W.[Wei], Qiu, Q.[Qiang], Huang, J.J.[Jia-Ji], Calderbank, R.[Robert], Sapiro, G.[Guillermo], Daubechies, I.[Ingrid],
LDMNet: Low Dimensional Manifold Regularized Neural Networks,
CVPR18(2743-2751)
IEEE DOI 1812
Training issues. Manifolds, Feature extraction, Training, Geometry, Neural networks, Training data. BibRef

Wang, X.D.[Xiao-Di], Li, C.[Ce], Mou, Y.P.[Yi-Peng], Zhang, B.C.[Bao-Chang], Han, J.G.[Jun-Gong], Liu, J.Z.[Jian-Zhuang],
Taylor Convolutional Networks for Image Classification,
WACV19(1271-1279)
IEEE DOI 1904
backpropagation, convolutional neural nets, image classification, image representation, back propagation algorithm, Information filtering BibRef

Wang, X.D.[Xiao-Di], Zhang, B.C.[Bao-Chang], Li, C.[Ce], Ji, R.R.[Rong-Rong], Han, J.G.[Jun-Gong], Liu, J.Z.[Jian-Zhuang], Cao, X.B.[Xian-Bin],
Modulated Convolutional Networks,
CVPR18(840-848)
IEEE DOI 1812
Computer vision, Pattern recognition BibRef

Wang, T., Yamaguchi, K., Ordonez, V.,
Feedback-Prop: Convolutional Neural Network Inference Under Partial Evidence,
CVPR18(898-907)
IEEE DOI 1812
Task analysis, Visualization, Computer vision, Neural networks, Radio frequency, Graphical models, Training BibRef

Feng, Y., Zhang, Z., Zhao, X., Ji, R., Gao, Y.,
GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition,
CVPR18(264-272)
IEEE DOI 1812
Shape, Cameras, Solid modeling, Convolutional neural networks, Arrays BibRef

Pan, J., Liu, S., Sun, D., Zhang, J., Liu, Y., Ren, J., Li, Z., Tang, J., Lu, H., Tai, Y., Yang, M.,
Learning Dual Convolutional Neural Networks for Low-Level Vision,
CVPR18(3070-3079)
IEEE DOI 1812
Image resolution, Task analysis, Atmospheric modeling, Signal resolution, Computational modeling, Visualization BibRef

Hu, Q.H.[Qing-Hao], Li, G.[Gang], Wang, P.S.[Pei-Song], Zhang, Y.F.[Yi-Fan], Cheng, J.[Jian],
Training Binary Weight Networks via Semi-Binary Decomposition,
ECCV18(XIII: 657-673).
Springer DOI 1810
BibRef

Dubey, A.[Abhimanyu], Chatterjee, M.[Moitreya], Ahuja, N.[Narendra],
Coreset-Based Neural Network Compression,
ECCV18(VII: 469-486).
Springer DOI 1810
BibRef

Coors, B.[Benjamin], Condurache, A.P.[Alexandru Paul], Geiger, A.[Andreas],
SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images,
ECCV18(IX: 525-541).
Springer DOI 1810
BibRef

Zhao, Y.[Yiru], Jin, Z.M.[Zhong-Ming], Qi, G.J.[Guo-Jun], Lu, H.T.[Hong-Tao], Hua, X.S.[Xian-Sheng],
An Adversarial Approach to Hard Triplet Generation,
ECCV18(IX: 508-524).
Springer DOI 1810
Distinguish similar images from different categories and different images from same category. BibRef

Luo, Z.X.[Zi-Xin], Shen, T.W.[Tian-Wei], Zhou, L.[Lei], Zhu, S.[Siyu], Zhang, R.Z.[Run-Ze], Yao, Y.[Yao], Fang, T.[Tian], Quan, L.[Long],
GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints,
ECCV18(IX: 170-185).
Springer DOI 1810
BibRef

Fu, Z., Ardabilian Fard, M.,
Learning Confidence Measures by Multi-modal Convolutional Neural Networks,
WACV18(1321-1330)
IEEE DOI 1806
convolution, feedforward neural nets, image colour analysis, image matching, learning (artificial intelligence), Training BibRef

Ji, J., Mei, S., Liu, X., Li, X., Zeng, S., Wang, Z.,
Exploring Kernel Based Spatial Context for CNN Based Hyperspectral Image Classification,
DICTA17(1-7)
IEEE DOI 1804
Gaussian processes, geophysical image processing, hyperspectral imaging, image classification, Training BibRef

Yang, L.X.[Ling-Xiao], Xie, X.H.[Xiao-Hua], Li, P.H.[Pei-Hua], Zhang, D.[David], Zhang, L.[Lei],
Part-Based Convolutional Neural Network for Visual Recognition,
ICIP17(1772-1776)
IEEE DOI 1803
Feature extraction, Image recognition, Optimization, Support vector machines, Task analysis, Training, Visualization, scene recognition BibRef

Liu, L., Rahimpour, A., Taalimi, A., Qi, H.,
End-to-end binary representation learning via direct binary embedding,
ICIP17(1257-1261)
IEEE DOI 1803
Binary codes, Convolutional neural networks, Entropy, Quantization (signal), Task analysis, Testing, Training, Multilabel Classification BibRef

Gatto, B.B., dos Santos, E.M.,
Discriminative canonical correlation analysis network for image classification,
ICIP17(4487-4491)
IEEE DOI 1803
Computer architecture, Correlation, Face, Feature extraction, Principal component analysis, Robustness, Training, image classification BibRef

Siadari, T.S., Han, M., Yoon, H.,
4D effect classification by encoding CNN features,
ICIP17(1812-1816)
IEEE DOI 1803
Feature extraction, Motion pictures, Support vector machines, Task analysis, Vibrations, Videos, Visualization, 4D Effect, Video Representation BibRef

Hsu, C.C.[Chih-Chung], Lin, C.W.[Chia-Wen],
Unsupervised convolutional neural networks for large-scale image clustering,
ICIP17(390-394)
IEEE DOI 1803
Complexity theory, Convolutional neural networks, Feature extraction, Graphics processing units, Machine learning, image clustering BibRef

Mitra, R.[Rahul], Zhang, J.[Jiakai], Narayan, S.[Sanath], Ahmed, S.[Shuaib], Chandran, S.[Sharat], Jain, A.[Arjun],
Improved Descriptors for Patch Matching and Reconstruction,
CEFR-LCV17(1023-1031)
IEEE DOI 1802
Feature extraction, Lighting, Measurement, Robustness, Training BibRef

Zoumpourlis, G., Doumanoglou, A., Vretos, N., Daras, P.,
Non-linear Convolution Filters for CNN-Based Learning,
ICCV17(4771-4779)
IEEE DOI 1802
approximation theory, convolution, image classification, image filtering, learning (artificial intelligence), neural nets, Visualization BibRef

Chen, Q., Xu, J., Koltun, V.,
Fast Image Processing with Fully-Convolutional Networks,
ICCV17(2516-2525)
IEEE DOI 1802
filtering theory, image processing, learning (artificial intelligence), neural nets, Training BibRef

Kong, T.[Tao], Sun, F.C.[Fu-Chun], Yao, A.B.[An-Bang], Liu, H.P.[Hua-Ping], Lu, M.[Ming], Chen, Y.R.[Yu-Rong],
RON: Reverse Connection with Objectness Prior Networks for Object Detection,
CVPR17(5244-5252)
IEEE DOI 1711
Detectors, Feature extraction, Object detection, Pipelines, Proposals, Search problems, Training BibRef

Tamaazousti, Y., Borgne, H.L., Hudelot, C.,
MuCaLe-Net: Multi Categorical-Level Networks to Generate More Discriminating Features,
CVPR17(5282-5291)
IEEE DOI 1711
Additives, Image edge detection, Image representation, Proposals, Psychology, Standards BibRef

Worrall, D.E.[Daniel E.], Brostow, G.J.[Gabriel J.],
CubeNet: Equivariance to 3D Rotation and Translation,
ECCV18(VI: 585-602).
Springer DOI 1810
BibRef

Zhou, Y.Z.[Yan-Zhao], Ye, Q.X.[Qi-Xiang], Qiu, Q.[Qiang], Jiao, J.B.[Jian-Bin],
Oriented Response Networks,
CVPR17(4961-4970)
IEEE DOI 1711
Rotating filters, produce feature maps with location and orientation. Active filters, Convolution, Convolutional codes, Neural networks, Training, Transforms BibRef

Ren, J.[Jimmy], Chen, X.H.[Xiao-Hao], Liu, J.B.[Jian-Bo], Sun, W.X.[Wen-Xiu], Pang, J.H.[Jia-Hao], Yan, Q.[Qiong], Tai, Y.W.[Yu-Wing], Xu, L.[Li],
Accurate Single Stage Detector Using Recurrent Rolling Convolution,
CVPR17(752-760)
IEEE DOI 1711
Computational modeling, Computer architecture, Convolution, Detectors, Feature extraction, Proposals, Robustness BibRef

Stone, A., Wang, H., Stark, M., Liu, Y., Phoenix, D.S., George, D.,
Teaching Compositionality to CNNs,
CVPR17(732-741)
IEEE DOI 1711
Airplanes, Context modeling, Standards, Training, Visualization 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

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

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

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

Song, Y.[Yan], Wang, P.S.[Pei-Seng], Hong, X.H.[Xin-Hai], McLoughlin, I.[Ian],
Fisher vector based CNN architecture for image classification,
ICIP17(565-569)
IEEE DOI 1803
Computer architecture, Convolutional codes, Encoding, Feature extraction, Probabilistic logic, Task analysis, Training, Visual Representation 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

Fernando, B.[Basura], Bilen, H.[Hakan], Gavves, E.[Efstratios], Gould, S.[Stephen],
Self-Supervised Video Representation Learning with Odd-One-Out Networks,
CVPR17(5729-5738)
IEEE DOI 1711
Cognition, Encoding, Learning systems, Manuals, Neural networks, Training 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Learning Non-maximum Suppression,
CVPR17(6469-6477)
IEEE DOI 1711
BibRef
Earlier:
A Convnet for Non-maximum Suppression,
GCPR16(192-204).
Springer DOI 1611
Detectors, Neural networks, Object detection, Proposals, Standards, Training 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

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

Marvasti, E.E., Marvasti, A.E., Foroosh, H.[Hassan],
Exploiting Symmetries of Distributions in CNNs and Folded Coding,
CRV18(47-54)
IEEE DOI 1812
Encoding, Data mining, Convolutional codes, Estimation, Random variables, Convolutional neural networks, Activation Functions 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

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

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

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

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

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
And:
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks,
CVPR14(1717-1724)
IEEE DOI 1409
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
Graph Convolutional Neural Networks .


Last update:Jul 10, 2020 at 16:03:35