14.5.10.8 Convolutional Neural Networks for Image Descriptions, Classification

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, DNN.
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.
See also Structural Description, Spatial Descriptions in Deep Networks.

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PR(131), 2022, pp. 108873.
Elsevier DOI 2208
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Elsevier DOI 2304
DNNs, Neuron modeling, Heuristic algorithm, Back propagation, Computer vision BibRef

Huang, G.[Gao], Liu, Z.[Zhuang], Pleiss, G.[Geoff], van der Maaten, L.[Laurens], Weinberger, K.Q.[Kilian Q.],
Convolutional Networks with Dense Connectivity,
PAMI(44), No. 12, December 2022, pp. 8704-8716.
IEEE DOI 2212
Convolution, Training, Computer architecture, Neural networks, Benchmark testing, Network architecture, Task analysis, image classification BibRef

Shi, X.S.[Xiao-Shuang], Guo, Z.H.[Zhen-Hua], Li, K.[Kang], Liang, Y.[Yun], Zhu, X.F.[Xiao-Feng],
Self-paced resistance learning against overfitting on noisy labels,
PR(134), 2023, pp. 109080.
Elsevier DOI 2212
Convolutional neural networks, Self-paced resistance, Model overfitting, Noisy labels BibRef

Yang, A.[Aitao], Li, M.[Min], Wu, Z.Q.[Zhao-Qing], He, Y.J.[Yu-Jie], Qiu, X.H.[Xiao-Hua], Song, Y.[Yu], Du, W.D.[Wei-Dong], Gou, Y.[Yao],
CDF-net: A convolutional neural network fusing frequency domain and spatial domain features,
IET-CV(17), No. 3, 2023, pp. 319-329.
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convolutional neural nets (CNN) BibRef

Zhu, M.[Meng], Min, W.D.[Wei-Dong], Han, J.W.[Jun-Wei], Han, Q.[Qing], Cui, S.M.[Shi-Miao],
Improved channel attention methods via hierarchical pooling and reducing information loss,
PR(148), 2024, pp. 110148.
Elsevier DOI 2402
Convolutional neural networks, Channel attention, Pooling, Reducing information loss, Information encoding BibRef


Cao, J.D.[Ji-Dong], He, C.[Chu], Pan, J.H.[Jia-Hao], Zhang, Q.Y.[Qing-Yi], Chen, X.[Xi],
Wavelet-Based Frequency-Dividing Interactive CNN for Image Classification,
ICIP23(2415-2419)
IEEE DOI 2312
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Singh, J.[Jaspreet], Singh, C.[Chandan], Rana, A.[Ankur],
Orthogonal Transforms For Learning Invariant Representations In Equivariant Neural Networks,
WACV23(1523-1530)
IEEE DOI 2302
Neural networks, Transforms, Harmonic analysis, Encoding, Reflection, Convolutional neural networks, visual reasoning BibRef

Hermosilla, P.[Pedro], Schelling, M.[Michael], Ritschel, T.[Tobias], Ropinski, T.[Timo],
Variance-Aware Weight Initialization for Point Convolutional Neural Networks,
ECCV22(XXVIII:74-89).
Springer DOI 2211
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Biswas, K.[Koushik], Kumar, S.[Sandeep], Banerjee, S.[Shilpak], Pandey, A.K.[Ashish Kumar],
SAU: Smooth Activation Function Using Convolution with Approximate Identities,
ECCV22(XXI:313-329).
Springer DOI 2211
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Chavan, A.[Arnav], Bamba, U.[Udbhav], Tiwari, R.[Rishabh], Gupta, D.[Deepak],
Rescaling CNN Through Learnable Repetition of Network Parameters,
ICIP21(754-758)
IEEE DOI 2201
Deep learning, Image processing, Neural networks, Performance gain, Numerical models, Convolutional neural networks, convolutional neural networks BibRef

Bennabhaktula, G.S.[Guru Swaroop], Antonisse, J.[Joey], Azzopardi, G.[George],
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator,
CAIP21(I:434-444).
Springer DOI 2112
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Sun, H.M.[Hui-Ming], Lin, Y.W.[Yue-Wei], Zou, Q.[Qin], Song, S.Y.[Shao-Yue], Fang, J.W.[Jian-Wu], Yu, H.K.[Hong-Kai],
Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments,
LUAI21(713-720)
IEEE DOI 2112
Training, Satellites, Clouds, Neural networks, Feature extraction, Convolutional neural networks, Remote sensing BibRef

Chen, S.N.[Shan-Nan], Wang, Q.[Qian], Sun, Q.[Qiule], Liu, B.[Bin], Zhang, J.X.[Jian-Xin], Zhang, Q.[Qiang],
Second-order Attention Guided Convolutional Activations for Visual Recognition,
ICPR21(3071-3076)
IEEE DOI 2105
Visualization, Tensors, Limiting, Network topology, Computational modeling, Image representation, visual recognition BibRef

Asad, M.[Muhammad], Basaru, R.[Rilwan], Al Arif, S.M.M.R.[S. M. Masudur Rahman], Slabaugh, G.[Greg],
PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks,
ICPR21(481-488)
IEEE DOI 2105
Training, Propulsion, Predictive models, Probabilistic logic, Probability distribution, Pattern recognition BibRef

Chakar, J.[Joseph], Al Sobbahi, R.[Rayan], Tekli, J.[Joe],
Depthwise Separable Convolutions and Variational Dropout within the context of YOLOv3,
ISVC20(I:107-120).
Springer DOI 2103
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Ma, N.N.[Ning-Ning], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
Funnel Activation for Visual Recognition,
ECCV20(XI:351-368).
Springer DOI 2011
Code, Image Recognition.
WWW Link. Extends ReLU and PReLU to a 2D activation. BibRef

Yan, N., Liu, D., Li, H., Wu, F.,
Semantically Scalable Image Coding With Compression of Feature Maps,
ICIP20(3114-3118)
IEEE DOI 2011
Image coding, Task analysis, Semantics, Visualization, Correlation, Decoding, Encoding, Convolutional neural network, image representation BibRef

Jawali, D.[Dhruv], Pokala, P.K.[Praveen Kumar], Seelamantula, C.S.[Chandra Sekhar],
CoRNet: Composite-Regularized Neural Network For Convolutional Sparse Coding,
ICIP20(818-822)
IEEE DOI 2011
Convolutional codes, Convolution, Image denoising, Neural networks, Image coding, Noise reduction, Minimization, CoRNet BibRef

Gao, Y.[Yue], Shi, J.[Jun], Li, J.[Jun], Wang, R.Y.[Ruo-Yu],
Remote Sensing Scene Classification with Dual Attention-Aware Network,
ICIVC20(171-175)
IEEE DOI 2009
Image analysis, Remote sensing, Semantics, Feature extraction, Training, Sensors, Convolutional neural networks, Remote sensing, convolutional neural network BibRef

Haase, D.[Daniel], Amthor, M.[Manuel],
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets,
CVPR20(14588-14597)
IEEE DOI 2008
Kernel, Correlation, Standards, Training, Redundancy, Computational modeling BibRef

Tong, Z.Q.[Zhi-Qiang], Tanaka, G.[Gouhei],
Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition,
ICPR18(1289-1294)
IEEE DOI 1812
Reservoirs, Feature extraction, Convolution, Image recognition, Computational modeling, Training, Convolutional neural networks BibRef

Xu, W., Wang, G., Sullivan, A., Zhang, Z.,
Towards Learning Affine-Invariant Representations via Data-Efficient CNNs,
WACV20(893-902)
IEEE DOI 2006
Training, Neural networks, Convolution, Knowledge engineering, Feature extraction, Robustness BibRef

Shen, Y.M.[Yu-Ming], Qin, J.[Jie], Chen, J.X.[Jia-Xin], Liu, L.[Li], Zhu, F.[Fan], Shen, Z.Y.[Zi-Yi],
Embarrassingly Simple Binary Representation Learning,
CEFRL19(2883-2892)
IEEE DOI 2004
learning (artificial intelligence), pattern classification, ImageNet benchmark, NUS-WIDE benchmark, CIFAR-10 benchmark, Representation Learning BibRef

Caron, M., Bojanowski, P., Mairal, J., Joulin, A.,
Unsupervised Pre-Training of Image Features on Non-Curated Data,
ICCV19(2959-2968)
IEEE DOI 2004
Code, Learning.
WWW Link. convolutional neural nets, image classification, unsupervised learning, unsupervised methods, Standards BibRef

Kaplanoglou, P.I., Diamantaras, K.,
Margin-Based Sample Filtering for Image Classification Using Convolutional Neural Networks,
ICIP18(1-5)
IEEE DOI 1809
Training, Filtering, Convolutional neural networks, Indexes, Visualization, Multiclass Margin BibRef

Eilertsen, G.[Gabriel], Mantiuk, R.K.[Rafal K.], Unger, J.[Jonas],
Single-Frame Regularization for Temporally Stable CNNs,
CVPR19(11168-11177).
IEEE DOI 2002
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Chen, Z.R.[Zhou-Rong], Li, Y.[Yang], Bengio, S.[Samy], Si, S.[Si],
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs,
CVPR19(9164-9172).
IEEE DOI 2002
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Su, H.[Hang], Jampani, V.[Varun], Sun, D.Q.[De-Qing], Gallo, O.[Orazio], Learned-Miller, E.G.[Erik G.], Kautz, J.[Jan],
Pixel-Adaptive Convolutional Neural Networks,
CVPR19(11158-11167).
IEEE DOI 2002
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Prakash, A.[Aaditya], Storer, J.[James], Florencio, D.[Dinei], Zhang, C.[Cha],
RePr: Improved Training of Convolutional Filters,
CVPR19(10658-10667).
IEEE DOI 2002
BibRef

Zhu, X.Z.[Xi-Zhou], Hu, H.[Han], Lin, S.[Stephen], Dai, J.F.[Ji-Feng],
Deformable ConvNets V2: More Deformable, Better Results,
CVPR19(9300-9308).
IEEE DOI 2002
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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],
Self-Supervised Convolutional Subspace Clustering Network,
CVPR19(5468-5477).
IEEE DOI 2002
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Hein, M.[Matthias], Andriushchenko, M.[Maksym], Bitterwolf, J.[Julian],
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem,
CVPR19(41-50).
IEEE DOI 2002
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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],
Hyperspectral Image Classification via Convolutional Neural Network Based on Dilation Layers,
CIAP19(I:378-387).
Springer DOI 1909
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Mendolia, I.[Isabella], Contino, S.[Salvatore], Perricone, U.[Ugo], Pirrone, R.[Roberto], Ardizzone, E.[Edoardo],
A Convolutional Neural Network for Virtual Screening of Molecular Fingerprints,
CIAP19(I:399-409).
Springer DOI 1909
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Anwer, R.M.[Rao Muhammad], Khan, F.S.[Fahad Shahbaz], Laaksonen, J.[Jorma], Zaki, N.[Nazar],
Multi-stream Convolutional Networks for Indoor Scene Recognition,
CAIP19(I:196-208).
Springer DOI 1909
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Yang, S.Q.[Shi-Qi], 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

Wang, M., Zhou, J., Mao, W., Gong, M.,
Multi-Scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets,
WACV19(321-330)
IEEE DOI 1904
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
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, 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

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

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
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

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, 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, 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
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.F.[Qi-Feng],
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.F.[Ji-Feng], 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

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

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 Neural Networks, GNN .


Last update:Mar 16, 2024 at 20:36:19