14.5.10.7 Convolutional Neural Networks for Image Descriptions, Classification

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

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

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

Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M.B., Liang, J.,
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally,
CVPR17(4761-4772)
IEEE DOI 1711
Biomedical imaging, Entropy, Labeling, Machine learning, Noise measurement, Training 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

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

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

Cai, X.X.[Xiu-Xia], Song, B.[Bin],
Image-based pencil drawing synthesized using convolutional neural network feature maps,
MVA(29), No. 3, April 2018, pp. 503-512.
WWW Link. 1804
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

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

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

Wang, L.M.[Li-Min], Guo, S.[Sheng], Huang, W.L.[Wei-Lin], Xiong, Y.J.[Yuan-Jun], Qiao, Y.[Yu],
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

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

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

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.J.[Qi-Jian],
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

Li, E.[Erzhu], Xia, J.S.[Jun-Shi], Du, P.J.[Pei-Jun], Lin, C.[Cong], Samat, A.[Alim],
Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification,
GeoRS(55), No. 10, October 2017, pp. 5653-5665.
IEEE DOI 1710
feature extraction, neural nets, principal component analysis, regression analysis, CNN model, Fisher kernel coding method, BibRef

Chen, J.[Jingbo], Wang, C.Y.[Cheng-Yi], Ma, Z.[Zhong], Chen, J.S.[Jian-Sheng], He, D.X.[Dong-Xu], Ackland, S.[Stephen],
Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Zhang, C.[Ce], Pan, X.[Xin], Li, H.P.[Hua-Peng], Gardiner, A.[Andy], Sargent, I.[Isabel], Hare, J.[Jonathon], Atkinson, P.M.[Peter M.],
A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,
PandRS(140), 2018, pp. 133-144.
Elsevier DOI 1805
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.],
Bag of Surrogate Parts Feature for Visual Recognition,
MultMed(20), No. 6, June 2018, pp. 1525-1536.
IEEE DOI 1805
Convolutional codes, Feature extraction, Neural networks, Semantics, Visualization, visual recognition BibRef

Hao, Y., Li, Q., Mo, H., Zhang, H., Li, H.,
AMI-Net: Convolution Neural Networks With Affine Moment Invariants,
SPLetters(25), No. 7, July 2018, pp. 1064-1068.
IEEE DOI 1807
affine transforms, 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
BibRef

Yang, H., Chen, T., Tu, C., Chen, C.,
Equivalent Scanning Network of Unpadded CNNs,
SPLetters(25), No. 10, October 2018, pp. 1590-1594.
IEEE DOI 1810
neural nets, signal processing, dilated convolutions, window mapping function, signal scanning theory, noble identity BibRef

Chen, T.[Tao], Lu, S.J.[Shi-Jian], Fan, J.Y.[Jia-Yuan],
SS-HCNN: Semi-Supervised Hierarchical Convolutional Neural Network for Image Classification,
IP(28), No. 5, May 2019, pp. 2389-2398.
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],
Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ren, Y.Y.[Yuan-Yuan], Zhang, X.F.[Xian-Feng], Ma, Y.J.[Yong-Jian], Yang, Q.Y.[Qi-Yuan], Wang, C.J.[Chuan-Jian], Liu, H.L.[Hai-Long], Qi, Q.[Quan],
Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zhang, A.[Anjun], Jia, L.[Lu], Wang, J.[Jun], Wang, C.J.[Chuan-Jian],
SAR Image Classification Using Gated Channel Attention Based Convolutional Neural Network,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Yuan, Y.[Yuan], Fang, J.[Jie], Lu, X.Q.[Xiao-Qiang], Feng, Y.C.[Ya-Chuang],
Remote Sensing Image Scene Classification Using Rearranged Local Features,
GeoRS(57), No. 3, March 2019, pp. 1779-1792.
IEEE DOI 1903
convolutional neural nets, feature extraction, geophysical image processing, image classification, representation scene classification BibRef

Huang, Y., Cao, X., Wang, Q., Zhang, B., Zhen, X., Li, X.,
Long-Short-Term Features for Dynamic Scene Classification,
CirSysVideo(29), No. 4, April 2019, pp. 1038-1047.
IEEE DOI 1904
Feature extraction, Dynamics, Vehicle dynamics, Task analysis, Convolutional neural networks, long term frequency feature BibRef

Liu, Y., Suen, C.Y., Liu, Y., Ding, L.,
Scene Classification Using Hierarchical Wasserstein CNN,
GeoRS(57), No. 5, May 2019, pp. 2494-2509.
IEEE DOI 1905
convolutional neural nets, entropy, geophysical image processing, image classification, interclass relationship BibRef

Gong, Z., Zhong, P., Yu, Y., Hu, W., Li, S.,
A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification,
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

Feng, X.J.[Xin-Jie], Yao, H.X.[Hong-Xun], Zhang, S.P.[Sheng-Ping],
An efficient way to refine DenseNet,
SIViP(13), No. 5, July 2019, pp. 959-965.
Springer DOI 1906

See also Densely Connected Convolutional Networks. BibRef

Oyallon, E.[Edouard], Zagoruyko, S.[Sergey], Huang, G.[Gabriel], Komodakis, N.[Nikos], Lacoste-Julien, S.[Simon], Blaschko, M.B.[Matthew B.], Belilovsky, E.[Eugene],
Scattering Networks for Hybrid Representation Learning,
PAMI(41), No. 9, Sep. 2019, pp. 2208-2221.
IEEE DOI 1908
Class of CNN with fixed weights. Scattering, Wavelet transforms, Task analysis, Pipelines, Gray-scale, Hybrid power systems, Scattering transform, wavelets, invariance BibRef

Zhang, H.K.[Hao-Kui], Li, Y.[Ying], Jiang, Y.[Yenan], Wang, P.[Peng], Shen, Q.[Qiang], Shen, C.H.[Chun-Hua],
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning,
GeoRS(57), No. 8, August 2019, pp. 5813-5828.
IEEE DOI 1908
convolutional neural nets, geophysical image processing, hyperspectral imaging, image classification, transfer learning BibRef

Minetto, R., Pamplona Segundo, M.[Mauricio], Sarkar, S.,
Hydra: An Ensemble of Convolutional Neural Networks for Geospatial Land Classification,
GeoRS(57), No. 9, September 2019, pp. 6530-6541.
IEEE DOI 1909
Training, Optimization, Head, Satellites, Convolutional neural networks, Geospatial analysis, remote sensing image classification BibRef

Xie, J.[Jie], He, N.J.[Nan-Jun], Fang, L.Y.[Le-Yuan], Plaza, A.J.[Antonio J.],
Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification,
GeoRS(57), No. 9, September 2019, pp. 6916-6928.
IEEE DOI 1909
Remote sensing, Feature extraction, Data models, Image color analysis, Kernel, Semantics, remote sensing scene classification BibRef

Lei, J.J.[Jian-Jun], Luo, X.W.[Xiao-Wei], Fang, L.Y.[Le-Yuan], Wang, M.Y.[Meng-Yuan], Gu, Y.F.[Yan-Feng],
Region-Enhanced Convolutional Neural Network for Object Detection in Remote Sensing Images,
GeoRS(58), No. 8, August 2020, pp. 5693-5702.
IEEE DOI 2007
Feature extraction, Object detection, Remote sensing, Image reconstruction, Nonhomogeneous media, Training, saliency constraint BibRef

Zhang, M., Li, W., Du, Q., Gao, L., Zhang, B.,
Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN,
Cyber(50), No. 1, January 2020, pp. 100-111.
IEEE DOI 1910
Feature extraction, Laser radar, Decoding, Hyperspectral imaging, Task analysis, multisensor fusion BibRef

Lu, X.Q.[Xiao-Qiang], Sun, H.[Hao], Zheng, X.T.[Xiang-Tao],
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

Zhang, Y.L.[Yuan-Lin], Zheng, X.T.[Xiang-Tao], Yuan, Y.[Yuan], Lu, X.Q.[Xiao-Qiang],
Attribute-Cooperated Convolutional Neural Network for Remote Sensing Image Classification,
GeoRS(58), No. 12, December 2020, pp. 8358-8371.
IEEE DOI 2012
Feature extraction, Task analysis, Convolutional neural networks, Remote sensing, Visualization, Semantics, Manuals, remote sensing image (RSI) classification BibRef

Lu, X.Q.[Xiao-Qiang], Gong, T.F.[Teng-Fei], Zheng, X.T.[Xiang-Tao],
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

Du, X.Q.[Xing-Qian], Zheng, X.T.[Xiang-Tao], Lu, X.Q.[Xiao-Qiang], Doudkin, A.A.[Alexander A.],
Multisource Remote Sensing Data Classification With Graph Fusion Network,
GeoRS(59), No. 12, December 2021, pp. 10062-10072.
IEEE DOI 2112
Feature extraction, Laser radar, Data mining, Fuses, Correlation, Task analysis, Dimensionality reduction, Classification, remote sensing BibRef

Sun, H.[Hao], Li, S.Y.[Si-Yuan], Zheng, X.T.[Xiang-Tao], Lu, X.Q.[Xiao-Qiang],
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

Wu, L.R.[Li-Rong], Liu, Z.C.[Zi-Cheng], Xia, J.[Jun], Zang, Z.L.[Ze-Lin], Li, S.Y.[Si-Yuan], Li, S.Z.[Stan Z.],
Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation,
WACV22(1668-1676)
IEEE DOI 2202
Manifolds, Measurement, Codes, Clustering algorithms, Biology, Deep Learning Clustering BibRef

Santa Cruz, R.[Rodrigo], Fernando, B.[Basura], Cherian, A.[Anoop], Gould, S.[Stephen],
Visual Permutation Learning,
PAMI(41), No. 12, December 2019, pp. 3100-3114.
IEEE DOI 1911
Visualization, Task analysis, Machine learning, Image sequences, Computational modeling, Predictive models, learning-to-rank BibRef

Moradi, R.[Reza], Berangi, R.[Reza], Minaei, B.[Behrooz],
OrthoMaps: an efficient convolutional neural network with orthogonal feature maps for tiny image classification,
IET-IPR(13), No. 12, October 2019, pp. 2067-2076.
DOI Link 1911
BibRef

Du, C.[Chen], Wang, Y.[Yanna], Wang, C.H.[Chun-Heng], Shi, C.Z.[Cun-Zhao], Xiao, B.H.[Bai-Hua],
Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector,
PRL(129), 2020, pp. 108-114.
Elsevier DOI 2001
Feature combination, Network architecture, Selective feature connection mechanism, Convolutional neural network BibRef

Rocco, I., Arandjelovic, R., Sivic, J.,
Convolutional Neural Network Architecture for Geometric Matching,
PAMI(41), No. 11, November 2019, pp. 2553-2567.
IEEE DOI 1910
BibRef
Earlier: CVPR17(39-48)
IEEE DOI 1711
Feature extraction, Correlation, Estimation, Convolutional neural networks, Geometry, Robustness, category-level matching. Correlationd, Geometry, Robustness BibRef

Bi, Q.[Qi], Qin, K.[Kun], Li, Z.[Zhili], Zhang, H.[Han], Xu, K.[Kai], Xia, G.S.[Gui-Song],
A Multiple-Instance Densely-Connected ConvNet for Aerial Scene Classification,
IP(29), 2020, pp. 4911-4926.
IEEE DOI 2003
BibRef
Earlier: A1, A2, A3, A4, A5, Only:
Multiple Instance Dense Connected Convolution Neural Network for Aerial Image Scene Classification,
ICIP19(2501-2505)
IEEE DOI 1910
23 layer network. Feature extraction, Semantics, Machine learning, Task analysis, Training, Neural networks, Visualization, Scene classification, aerial image. Scene classification, convolution neural network, dense connection, multiple instance learning. BibRef

Sun, R.[Rémy], Lampert, C.H.[Christoph H.],
KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications,
IJCV(128), No. 4, April 2020, pp. 970-995.
Springer DOI 2004
BibRef
And: Correction: IJCV(128), No. 4, April 2020, pp. 996.
Springer DOI 2004
BibRef
Earlier:
KS(conf): A Light-Weight Test if a ConvNet Operates Outside of Its Specifications,
GCPR18(244-259).
Springer DOI 1905
What happens when system runs for days or years and data quality changes. BibRef

Xu, H.Y.[Hong-Yu], Wang, Z.Y.[Zhang-Yang], Yang, H.C.[Hai-Chuan], Liu, D.[Ding], Liu, J.[Ji],
Learning Simple Thresholded Features With Sparse Support Recovery,
CirSysVideo(30), No. 4, April 2020, pp. 970-982.
IEEE DOI 2004
Encoding, Dictionaries, Machine learning, Standards, Time complexity, Task analysis, Sparse representation, feature learning, unsupervised learning BibRef

Li, W.J.[Wen-Juan], Li, B.[Bing], Yuan, C.F.[Chun-Feng], Li, Y.X.[Yang-Xi], Wu, H.H.[Hao-Hao], Hu, W.M.[Wei-Ming], Wang, F.S.[Fang-Shi],
Anisotropic Convolution for Image Classification,
IP(29), 2020, pp. 5584-5595.
IEEE DOI 2005
Convolution, Shape, Kernel, Feature extraction, Task analysis, Training, Neural networks, Anisotropic convolution, object localization BibRef

Peng, X.S.[Xu-Shan], Zhang, X.M.[Xiao-Ming], Li, Y.P.[Yong-Ping], Liu, B.Q.[Bang-Quan],
Research on image feature extraction and retrieval algorithms based on convolutional neural network,
JVCIR(69), 2020, pp. 102705.
Elsevier DOI 2006
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,
MultMed(22), No. 7, July 2020, pp. 1704-1719.
IEEE DOI 2007
Image color analysis, Task analysis, Image denoising, Learning systems, Image restoration, Convolution, saliency-driven image editing BibRef

Li, X.O.[Xia-Obin], Wang, W.Q.[Wei-Qiang],
Learning discriminative features via weights-biased softmax loss,
PR(107), 2020, pp. 107405.
Elsevier DOI 2008
Classification, Softmax, CNNs, Fully connected layer units, Classifier weights BibRef

Shao, W.Q.[Wen-Qi], Li, J.Y.[Jing-Yu], Ren, J.M.[Jia-Min], Zhang, R.M.[Rui-Mao], Wang, X.G.[Xiao-Gang], Luo, P.[Ping],
SSN: Learning Sparse Switchable Normalization via SparsestMax,
IJCV(128), No. 8-9, September 2020, pp. 2107-2125.
Springer DOI 2008
BibRef

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],
SSN: Learning Sparse Switchable Normalization via SparsestMax,
CVPR19(443-451).
IEEE DOI 2002
Code:
WWW Link. BibRef

Pei, Y.T.[Yan-Ting], Huang, Y.P.[Ya-Ping], Zou, Q.[Qi], Zhang, X.Y.[Xing-Yuan], Wang, S.[Song],
Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification,
PAMI(43), No. 4, April 2021, pp. 1239-1253.
IEEE DOI 2103
Degradation, Cameras, Image resolution, Image recognition, Training, Deep learning, Image classification, CNN BibRef

Zhang, X.[Xiang], Tang, L.[Lei], Luo, H.Z.[Hang-Zai], Zhong, S.[Sheng], Guan, Z.Y.[Zi-Yu], Chen, L.[Long], Zhao, C.[Chao], Peng, J.Y.[Jin-Ye], Fan, J.P.[Jian-Ping],
Hierarchical Bilinear Convolutional Neural Network for Image Classification,
IET-CV(15), No. 3, 2021, pp. 197-207.
DOI Link 2106
BibRef

Wu, X.[Xuan], Zhang, Z.J.[Zhi-Jie], Zhang, W.C.[Wan-Chang], Yi, Y.N.[Ya-Ning], Zhang, C.R.[Chuan-Rong], Xu, Q.[Qiang],
A Convolutional Neural Network Based on Grouping Structure for Scene Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Bi, Q.[Qi], Qin, K.[Kun], Zhang, H.[Han], Xia, G.S.[Gui-Song],
Local Semantic Enhanced ConvNet for Aerial Scene Recognition,
IP(30), 2021, pp. 6498-6511.
IEEE DOI 2107
Semantics, Feature extraction, Visualization, MIMICs, Image recognition, Task analysis, Deep learning, multi-level convolutional feature fusion BibRef

Hao, Y.[You], Hu, P.[Ping], Li, S.R.[Shi-Rui], Udupa, J.K.[Jayaram K.], Tong, Y.B.[Yu-Bing], Li, H.[Hua],
Gradient-Aligned convolution neural network,
PR(122), 2022, pp. 108354.
Elsevier DOI 2112
Gradient alignment, Rotation equivariant convolution, Rotation invariant neural network BibRef

Lu, H.[Hao], Dai, Y.T.[Yu-Tong], Shen, C.H.[Chun-Hua], Xu, S.C.[Song-Cen],
Index Networks,
PAMI(44), No. 1, January 2022, pp. 242-255.
IEEE DOI 2112
Upsampling operators in convolutional networks can be unified using the notion of the index function. Indexes, Task analysis, Interpolation, Semantics, Image segmentation, Convolution, Estimation, Upsampling operators, dynamic networks, depth estimation BibRef

Ma, Y.L.[Yun-Long], Wang, C.Y.[Chun-Yan],
SdcNet for object recognition,
CVIU(215), 2022, pp. 103332.
Elsevier DOI 2201
Convolutional neural network (CNN), Image processing, Object recognition, Feature extraction, Machine learning BibRef

Shuang, F.[Feng], Huang, H.Z.[Han-Zhang], Li, Y.[Yong], Qu, R.[Rui], Li, P.[Pei],
AFE-RCNN: Adaptive Feature Enhancement RCNN for 3D Object Detection,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Zheng, T.Y.[Tian-You], Wang, Q.[Qiang], Shen, Y.[Yue], Ma, X.[Xiang], Lin, X.T.[Xiao-Tian],
Batch covariance neural network for image recognition,
IVC(122), 2022, pp. 104446.
Elsevier DOI 2205
CNN, BCovNN, Batch covariance, Illumination intensity, Feature interaction BibRef

Wang, L.Y.[Lu-Yuan], Sun, Y.K.[Yan-Kui],
Image classification using convolutional neural network with wavelet domain inputs,
IET-IPR(16), No. 8, 2022, pp. 2037-2048.
DOI Link 2205
BibRef

Pan, J.S.[Jin-Shan], Sun, D.Q.[De-Qing], Zhang, J.W.[Jia-Wei], Tang, J.H.[Jin-Hui], Yang, J.[Jian], Tai, Y.W.[Yu-Wing], Yang, M.H.[Ming-Hsuan],
Dual Convolutional Neural Networks for Low-Level Vision,
IJCV(130), No. 6, June 2022, pp. 1440-1458.
Springer DOI 2207
BibRef

Pan, J.S.[Jin-Shan], Liu, S.F.[Si-Fei], Sun, D.Q.[De-Qing], Zhang, J.W.[Jia-Wei], Liu, Y.[Yang], Ren, J.[Jimmy], Li, Z.C.[Ze-Chao], Tang, J.H.[Jin-Hui], Lu, H.C.[Hu-Chuan], Tai, Y.W.[Yu-Wing], Yang, M.H.[Ming-Hsuan],
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

Wang, H.[Hong], Gao, K.[Kun], Min, L.[Lei], Mao, Y.X.[Yu-Xuan], Zhang, X.D.[Xiao-Dian], Wang, J.W.[Jun-Wei], Hu, Z.[Zibo], Liu, Y.T.[Yu-Tong],
Triplet-Metric-Guided Multi-Scale Attention for Remote Sensing Image Scene Classification with a Convolutional Neural Network,
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DNNs, Neuron modeling, Heuristic algorithm, Back propagation, Computer vision BibRef

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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.
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Convolutional neural networks, Self-paced resistance, Model overfitting, Noisy labels BibRef

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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

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Variance-Aware Weight Initialization for Point Convolutional Neural Networks,
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Biswas, K.[Koushik], Kumar, S.[Sandeep], Banerjee, S.[Shilpak], Pandey, A.K.[Ashish Kumar],
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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,
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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).
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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).
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Code, Image Recognition.
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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).
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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
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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,
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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).
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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).
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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).
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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).
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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).
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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).
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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
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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
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Walach, E.[Elad], Wolf, L.B.[Lior B.],
Learning to Count with CNN Boosting,
ECCV16(II: 660-676).
Springer DOI 1611
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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)
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Goroshin, R.[Ross], Bruna, J.[Joan], Tompson, J.[Jonathan], Eigen, D.[David], Le Cun, Y.L.[Yann L.],
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ICCV15(4086-4093)
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Convolution BibRef

Wang, X.L.[Xiao-Long], Gupta, A.[Abhinav],
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ICCV15(2794-2802)
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Zha, S.X.[Sheng-Xin], Luisier, F.[Florian], Andrews, W.[Walter], Srivastava, N.[Nitish], Salakhutdinov, R.[Ruslan],
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Graham, B.[Ben],
Sparse 3D convolutional neural networks,
BMVC15(xx-yy).
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Kang, S.H.[Sung-Hun], Lee, D.H.[Dong-Hoon], Yoo, C.D.[Chang D.],
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ICIP15(4922-4926)
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Peng, K.C.[Kuan-Chuan], Chen, T.H.[Tsu-Han],
Toward correlating and solving abstract tasks using convolutional neural networks,
WACV16(1-9)
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Cross-layer features in convolutional neural networks for generic classification tasks,
ICIP15(3057-3061)
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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)
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Local distortion visibility BibRef

Hosang, J.[Jan], Benenson, R.[Rodrigo], Schiele, B.[Bernt],
Learning Non-maximum Suppression,
CVPR17(6469-6477)
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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],
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He, K.M.[Kai-Ming], Sun, J.[Jian],
Convolutional neural networks at constrained time cost,
CVPR15(5353-5360)
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CRV18(47-54)
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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],
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CVPR15(806-814)
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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)
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Accuracy BibRef

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

Jie, Z.[Zequn], Yan, S.C.[Shui-Cheng],
Robust Scene Classification with Cross-Level LLC Coding on CNN Features,
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Springer DOI 1504
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Ozeki, M.[Makoto], Okatani, T.[Takayuki],
Understanding Convolutional Neural Networks in Terms of Category-Level Attributes,
ACCV14(II: 362-375).
Springer DOI 1504
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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,
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DropAll: Generalization of Two Convolutional Neural Network Regularization Methods,
ICIAR14(I: 282-289).
Springer DOI 1410
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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
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And:
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks,
CVPR14(1717-1724)
IEEE DOI 1409
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Simard, P.Y., Steinkraus, D., Platt, J.C.,
Best practices for convolutional neural networks applied to visual document analysis,
ICDAR03(958-963).
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Graph Neural Networks .