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
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.B.[Jing-Bo],
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
Wang, F.[Fang],
Du, X.Q.[Xing-Qian],
Zhang, W.G.[Wei-Guang],
Nie, L.[Liang],
Wang, H.[Hu],
Zhou, S.[Shun],
Ma, J.[Jun],
Remote Sensing LiDAR and Hyperspectral Classification with
Multi-Scale Graph Encoder-Decoder Network,
RS(16), No. 20, 2024, pp. 3912.
DOI Link
2411
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,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Ning, X.[Xin],
Tian, W.J.[Wei-Juan],
Yu, Z.Y.[Zai-Yang],
Li, W.J.[Wei-Jun],
Bai, X.[Xiao],
Wang, Y.B.[Yue-Bao],
HCFNN: High-order coverage function neural network for image
classification,
PR(131), 2022, pp. 108873.
Elsevier DOI
2208
BibRef
And:
Corrigendum:
PR(139), 2023, pp. 109433.
Elsevier DOI
2304
DNNs, Neuron modeling, Heuristic algorithm, Back propagation
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
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.
DOI Link
2305
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
He, Y.P.[Yuan-Peng],
Song, W.J.[Wen-Jie],
Li, L.J.[Li-Jian],
Zhan, T.X.[Tian-Xiang],
Jiao, W.[Wenpin],
Residual Feature-Reutilization Inception Network,
PR(152), 2024, pp. 110439.
Elsevier DOI
2405
Feature-reutilization, Residual connection, Inception
BibRef
Xiao, F.[Fen],
Li, X.[Xiang],
Li, W.[Wei],
Shi, J.J.[Jun-Jie],
Zhang, N.[Ningru],
Gao, X.[Xieping],
Integrating category-related key regions with a dual-stream network
for remote sensing scene classification,
JVCIR(100), 2024, pp. 104098.
Elsevier DOI
2405
Remote sensing scene classification, Swin transformer,
Convolutional neural network, Dual-stream,
Category-related key region localization
BibRef
Xiong, H.[Huan],
Huang, L.[Lei],
Zang, W.J.T.[Wenston J.T.],
Zhen, X.T.[Xian-Tong],
Xie, G.S.[Guo-Sen],
Gu, B.[Bin],
Song, L.[Le],
On the Number of Linear Regions of Convolutional Neural Networks With
Piecewise Linear Activations,
PAMI(46), No. 7, July 2024, pp. 5131-5148.
IEEE DOI
2406
Upper bound, Convolutional neural networks, Task analysis, Neurons,
Network architecture, Distortion measurement, Deep learning,
piecewise linear
BibRef
Wang, G.W.[Guo-Wei],
Shi, F.[Furong],
Wang, X.Y.[Xin-Yu],
Xu, H.X.[Hai-Xia],
Yuan, L.M.[Li-Ming],
Wen, X.B.[Xian-Bin],
LPNet: A remote sensing scene classification method based on large
kernel convolution and parameter fusion,
IET-IPR(18), No. 9, 2024, pp. 2417-2433.
DOI Link
2407
convolutional neural nets,
image classification, remote sensing
BibRef
Molek, V.[Vojtech],
Alijani, Z.[Zahra],
Fractional concepts in neural networks:
Enhancing activation functions,
PRL(190), 2025, pp. 126-132.
Elsevier DOI
2503
Fractional calculus, Convolutional neural network,
Activation function, Image classification
BibRef
Alexandridis, K.P.[Konstantinos Panagiotis],
Deng, J.K.[Jian-Kang],
Nguyen, A.[Anh],
Luo, S.[Shan],
Adaptive Parametric Activation,
ECCV24(LIV: 455-476).
Springer DOI
2412
Code:
WWW Link.
BibRef
Li, Y.[Yikang],
Qiu, Y.Q.[Ye-Qing],
Chen, Y.X.[Yu-Xuan],
He, L.[Lingshen],
Lin, Z.C.[Zhou-Chen],
Affine Equivariant Networks Based on Differential Invariants,
CVPR24(5546-5556)
IEEE DOI
2410
Manifolds, Accuracy, Lie groups, Polynomials,
equivariant networks, differential invariants, affine group
BibRef
Ding, X.H.[Xiao-Han],
Zhang, Y.Y.[Yi-Yuan],
Ge, Y.X.[Yi-Xiao],
Zhao, S.[Sijie],
Song, L.[Lin],
Yue, X.Y.[Xiang-Yu],
Shan, Y.[Ying],
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio,
Video, Point Cloud, Time-Series and Image Recognition,
CVPR24(5513-5524)
IEEE DOI
2410
Training, Point cloud compression, Image recognition,
Transformer cores, Predictive models, Transformers, Multimodal Learning
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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,
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.Q.[Ze-Qun],
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 .