14.5.9.7.14 Structural Description, Spatial Descriptions in Deep Networks

Chapter Contents (Back)
Deep Nets. Structural Descriptions. Spatial Descriptions.

Takatsuka, M., Jarvis, R.A.,
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Elsevier DOI 1203
Award, King Sun Fu. BibRef
Earlier:
On refining dissimilarity matrices for an improved NN learning,
ICPR08(1-4).
IEEE DOI 0812
Dissimilarity representation; Representation set; Dissimilarity space; Vector space; Structural pattern recognition BibRef

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On the Latent Variable Interpretation in Sum-Product Networks,
PAMI(39), No. 10, October 2017, pp. 2030-2044.
IEEE DOI 1709
Bayes methods, Computational modeling, Inference algorithms, Mixture models, Periodic structures, Probabilistic logic, Semantics, MPE inference, Sum-product networks, expectation-maximization, latent variables, mixture, models
See also Sum-product networks: A new deep architecture. BibRef

Lu, J.[Jie], Xuan, J.Y.[Jun-Yu], Zhang, G.Q.[Guang-Quan], Luo, X.F.[Xiang-Feng],
Structural property-aware multilayer network embedding for latent factor analysis,
PR(76), No. 1, 2018, pp. 228-241.
Elsevier DOI 1801
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Cheng, G., Li, Z., Han, J., Yao, X., Guo, L.,
Exploring Hierarchical Convolutional Features for Hyperspectral Image Classification,
GeoRS(56), No. 11, November 2018, pp. 6712-6722.
IEEE DOI 1811
Feature extraction, Measurement, Support vector machines, Training, Machine learning, Semantics, Hyperspectral imaging, spectral-spatial feature BibRef

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

Liu, Y.[Yang], Luo, T.J.[Tie-Jian],
The optimization of sum-product network structure learning,
JVCIR(60), 2019, pp. 391-397.
Elsevier DOI 1903
Machine learning, Deep learning, Sum-product network, Structure learning BibRef

Yang, J.[Jihai], Xiong, W.[Wei], Li, S.J.[Shi-Jun], Xu, C.[Chang],
Learning structured and non-redundant representations with deep neural networks,
PR(86), 2019, pp. 224-235.
Elsevier DOI 1811
Deep networks, Overfitting, Decorrelation BibRef

Gong, M.[Maoguo], Yao, C.Y.[Chuan-Yu], Xie, Y.[Yu], Xu, M.L.[Ming-Liang],
Semi-supervised network embedding with text information,
PR(104), 2020, pp. 107347.
Elsevier DOI 2005
Network embedding, Structure preserving, Text representation, Stacked auto-encoders BibRef

Iddianozie, C.[Chidubem], McArdle, G.[Gavin],
Improved Graph Neural Networks for Spatial Networks Using Structure-Aware Sampling,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
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Gui, Y.[Yan], Zeng, G.[Guang],
Joint learning of visual and spatial features for edit propagation from a single image,
VC(36), No. 3, March 2020, pp. 469-482.
Springer DOI 2002
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Tabernik, D.[Domen], Kristan, M.[Matej], Leonardis, A.[Aleš],
Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks,
IJCV(128), No. 8-9, September 2020, pp. 2049-2067.
Springer DOI 2008
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Earlier:
Spatially-Adaptive Filter Units for Deep Neural Networks,
CVPR18(9388-9396)
IEEE DOI 1812
Standards, Task analysis, Kernel, Neural networks, Graphics processing units, Strain, Interpolation BibRef

Dai, Y.P.[Yong-Peng], Jin, T.[Tian], Song, Y.K.[Yong-Kun], Sun, S.L.[Shi-Long], Wu, C.[Chen],
Convolutional Neural Network with Spatial-Variant Convolution Kernel,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Ruan, D.S.[Dong-Sheng], Shi, Y.[Yu], Wen, J.[Jun], Zheng, N.G.[Neng-Gan], Zheng, M.[Min],
Spatially-Aware Context Neural Networks,
IP(30), 2021, pp. 6906-6916.
IEEE DOI 2108
Context modeling, Convolution, Semantics, Computational modeling, Transforms, Task analysis, Object detection, context modeling BibRef

Zhang, M.H.[Ming-Hua], Luo, H.[Hongling], Song, W.[Wei], Mei, H.B.[Hai-Bin], Su, C.[Cheng],
Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
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Wang, D.[Di], Lan, J.H.[Jin-Hui],
A Deformable Convolutional Neural Network with Spatial-Channel Attention for Remote Sensing Scene Classification,
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
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Vidal Pino, O.[Omar], Nascimento, E.R.[Erickson R.], Campos, M.F.M.[Mario F.M.],
Introducing the structural bases of typicality effects in deep learning,
IVC(113), 2021, pp. 104249.
Elsevier DOI 2108
Typicality effects, Category semantic representation, Image semantic representation, Semantic classification, Prototype theory BibRef

Ma, X.[Xu], Guo, J.[Jingda], Sansom, A.[Andrew], McGuire, M.[Mara], Kalaani, A.[Andrew], Chen, Q.[Qi], Tang, S.[Sihai], Yang, Q.[Qing], Fu, S.[Song],
Spatial Pyramid Attention for Deep Convolutional Neural Networks,
MultMed(23), 2021, pp. 3048-3058.
IEEE DOI 2109
Object detection, Feature extraction, Convolutional codes, Computer architecture, Benchmark testing, Topology, Task analysis, structural information BibRef

Ma, W.P.[Wen-Ping], Zhao, J.[Jiliang], Zhu, H.[Hao], Shen, J.C.[Jian-Chao], Jiao, L.C.[Li-Cheng], Wu, Y.[Yue], Hou, B.[Biao],
A Spatial-Channel Collaborative Attention Network for Enhancement of Multiresolution Classification,
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DOI Link 2101
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Zhu, S.[Sihan], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Li, X.[Xue],
Attention-Based Multiscale Residual Adaptation Network for Cross-Scene Classification,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Feature extraction, Task analysis, Data mining, Adaptation models, Transfer learning, Periodic structures, Manifolds, residual learning BibRef

Eberle, O.[Oliver], Büttner, J.[Jochen], Kräutli, F.[Florian], Müller, K.R.[Klaus-Robert], Valleriani, M.[Matteo], Montavon, G.[Grégoire],
Building and Interpreting Deep Similarity Models,
PAMI(44), No. 3, March 2022, pp. 1149-1161.
IEEE DOI 2202
Machine learning, Data models, Robustness, Neural networks, Taylor series, Feature extraction, Deep learning, Similarity, digital humanities BibRef


Melnyk, P.[Pavlo], Felsberg, M.[Michael], Wadenbäck, M.[Mårten],
Embed Me If You Can: A Geometric Perceptron,
ICCV21(1256-1264)
IEEE DOI 2203
Geometry, Solid modeling, Shape, Computational modeling, Neurons, Nonhomogeneous media, Recognition and classification, Machine learning architectures and formulations BibRef

Li, Y.[Yang], Kan, S.C.[Shi-Chao], Yuan, J.[Jianhe], Cao, W.M.[Wen-Ming], He, Z.H.[Zhi-Hai],
Spatial Assembly Networks for Image Representation Learning,
CVPR21(13871-13880)
IEEE DOI 2111
Measurement, Deep learning, Training, Visualization, Layout, Image retrieval, Transforms BibRef

Finnveden, L.[Lukas], Jansson, Y.[Ylva], Lindeberg, T.[Tony],
Understanding when spatial transformer networks do not support invariance, and what to do about it,
ICPR21(3427-3434)
IEEE DOI 2105
a way to do translation invariance in CNN. Location awareness, Transforms, Complexity theory, Convolutional neural networks BibRef

Ge, Y.H.[Yun-Hao], Xiao, Y.[Yao], Xu, Z.[Zhi], Zheng, M.[Meng], Karanam, S.[Srikrishna], Chen, T.[Terrence], Itti, L.[Laurent], Wu, Z.[Ziyan],
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts,
CVPR21(2195-2204)
IEEE DOI 2111
Deep learning, Bridges, Visualization, Correlation, Decision making, Artificial neural networks BibRef

Li, Y.[Yang], Tanaka, Y.[Yuichi],
Structural Features in Feature Space for Structure-Aware Graph Convolution,
ICIP21(3158-3162)
IEEE DOI 2201
Convolution, Image processing, Signal processing algorithms, Graph neural networks, Classification algorithms, deep learning BibRef

Patil, A.G.[Akshay Gadi], Li, M.[Manyi], Fisher, M.[Matthew], Savva, M.[Manolis], Zhang, H.[Hao],
LayoutGMN: Neural Graph Matching for Structural Layout Similarity,
CVPR21(11043-11052)
IEEE DOI 2111
Measurement, Deep learning, Convolution, Computational modeling, Layout, Graph neural networks BibRef

Carbonell, M.[Manuel], Riba, P.[Pau], Villegas, M.[Mauricio], Fornés, A.[Alicia], Lladós, J.[Josep],
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents,
ICPR21(9622-9627)
IEEE DOI 2105
Information retrieval, Graph neural networks, Data mining, Task analysis, Tuning, Testing, Strain, Graph Neural Networks BibRef

Fan, Y.[Yue], Xian, Y.Q.[Yong-Qin], Losch, M.M.[Max Maria], Schiele, B.[Bernt],
Analyzing the Dependency of ConvNets on Spatial Information,
GCPR20(101-115).
Springer DOI 2110
BibRef

Schneider, J.[Johannes],
Locality-Promoting Representation Learning,
ICPR21(8061-8068)
IEEE DOI 2105
Spatial filters, Convolutional neural networks BibRef

Chelali, M.[Mohamed], Kurtz, C.[Camille], Puissant, A.[Anne], Vincent, N.[Nicole],
Classification of spatially enriched pixel time series with convolutional neural networks,
ICPR21(5310-5317)
IEEE DOI 2105
Visualization, Image segmentation, Image color analysis, Time series analysis, Soil, Feature extraction, Spatiotemporal phenomena BibRef

Chi, L.[Lu], Yuan, Z.H.[Ze-Huan], Mu, Y.D.[Ya-Dong], Wang, C.H.[Chang-Hu],
Non-Local Neural Networks With Grouped Bilinear Attentional Transforms,
CVPR20(11801-11810)
IEEE DOI 2008
Model spatial. Convolution, Transforms, Kernel, Computer architecture, Task analysis, Biological neural networks BibRef

Mukundan, A.[Arun], Tolias, G.[Giorgos], Chum, O.[Ondrej],
Explicit Spatial Encoding for Deep Local Descriptors,
CVPR19(9386-9395).
IEEE DOI 2002
BibRef

Shevlev, I.[Irina], Avidan, S.[Shai],
Co-Occurrence Neural Network,
CVPR19(4792-4799).
IEEE DOI 2002
Adding spatial information. BibRef

Zhu, X., Cheng, D., Zhang, Z., Lin, S., Dai, J.,
An Empirical Study of Spatial Attention Mechanisms in Deep Networks,
ICCV19(6687-6696)
IEEE DOI 2004
convolution, image retrieval, neural nets, spatial attention mechanisms, deep neural networks, Natural language processing BibRef

Xu, Y., Xu, D., Hong, X., Ouyang, W., Ji, R., Xu, M., Zhao, G.,
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection,
ICCV19(3788-3797)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), message passing, object detection, structured modeling BibRef

Shah, S.A.A.[Syed Afaq Ali],
Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition,
PSIVT19(183-193).
Springer DOI 2003
BibRef

Huang, Z.[Zehao], Wang, N.[Naiyan],
Data-Driven Sparse Structure Selection for Deep Neural Networks,
ECCV18(XVI: 317-334).
Springer DOI 1810
BibRef

Wang, Y.[Yan], Xie, L.X.[Ling-Xi], Qiao, S.Y.[Si-Yuan], Zhang, Y.[Ya], Zhang, W.J.[Wen-Jun], Yuille, A.L.[Alan L.],
Multi-scale Spatially-Asymmetric Recalibration for Image Classification,
ECCV18(XIII: 523-539).
Springer DOI 1810
To get spatial information in features using multiple scales. BibRef

Zhao, H.S.[Heng-Shuang], Zhang, Y.[Yi], Liu, S.[Shu], Shi, J.P.[Jian-Ping], Loy, C.C.[Chen Change], Lin, D.[Dahua], Jia, J.Y.[Jia-Ya],
PSANet: Point-wise Spatial Attention Network for Scene Parsing,
ECCV18(IX: 270-286).
Springer DOI 1810
BibRef

Zhang, J.Y.[Jing-Yang], Jia, K.G.[Kai-Ge], Yang, P.S.[Peng-Shuai], Qiao, F.[Fei], Wei, Q.[Qi], Liu, X.J.[Xin-Jun], Yang, H.Z.[Hua-Zhong],
MINTIN: Maxout-Based and Input-Normalized Transformation Invariant Neural Network,
ICIP18(3014-3018)
IEEE DOI 1809
Need to deal with spatial variance in input. Feature extraction, Neural networks, Network topology, Error analysis, Kernel, Calibration, Maxout BibRef

Zhang, Y., Guo, Y., Jin, Y., Luo, Y., He, Z., Lee, H.,
Unsupervised Discovery of Object Landmarks as Structural Representations,
CVPR18(2694-2703)
IEEE DOI 1812
Visualization, Neural networks, Decoding, Image reconstruction, Training, Detectors BibRef

Xu, J.W.[Jing-Wei], Ni, B.B.[Bing-Bing], Li, Z.F.[Ze-Fan], Cheng, S.[Shuo], Yang, X.K.[Xiao-Kang],
Structure Preserving Video Prediction,
CVPR18(1460-1469)
IEEE DOI 1812
RNN structure. Kernel, Task analysis, Computer architecture, Dynamics, Decoding, Predictive models BibRef

Mughees, A., Tao, L.,
Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning,
DICTA17(1-6)
IEEE DOI 1804
BibRef
And:
Hyper-voxel based deep learning for hyperspectral image classification,
ICIP17(840-844)
IEEE DOI 1803
geophysical image processing, Training. feature extraction, hyperspectral imaging, image classification, image segmentation, learning (artificial intelligence), stacked auto-encoder BibRef

Mughees, A., Ali, A., Tao, L.,
Hyperspectral image classification via shape-adaptive deep learning,
ICIP17(375-379)
IEEE DOI 1803
Feature extraction, Hyperspectral imaging, Image segmentation, Machine learning, Spatial resolution, Training, segmentation BibRef

Gidaris, S.[Spyros], Komodakis, N.[Nikos],
Detect, Replace, Refine: Deep Structured Prediction for Pixel Wise Labeling,
CVPR17(7187-7196)
IEEE DOI 1711
Estimation, Iron, Labeling, Neural networks, Predictive, models BibRef

Zhu, Y., Zhao, C., Wang, J., Zhao, X., Wu, Y., Lu, H.,
CoupleNet: Coupling Global Structure with Local Parts for Object Detection,
ICCV17(4146-4154)
IEEE DOI 1802
convolution, image classification, neural nets, object detection, Convolutional Neural Network detectors, CoupleNet, Visualization BibRef

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

Hu, H.X.[He-Xiang], Zhou, G.T.[Guang-Tong], Deng, Z.W.[Zhi-Wei], Liao, Z.C.[Zi-Cheng], Mori, G.[Greg],
Learning Structured Inference Neural Networks with Label Relations,
CVPR16(2960-2968)
IEEE DOI 1612
Network for each layer of representation. BibRef

Kuzmenko, A., Zagoruyko, N.,
Structure relaxation method for self-organizing neural networks,
ICPR04(IV: 589-592).
IEEE DOI 0409
BibRef

Kröner, S.[Sabine],
A structured neural network invariant to cyclic shifts and rotations,
CAIP97(384-391).
Springer DOI 9709
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Loss Functions, Triplet Loss Function, Deep Learning, Neural Netowrks .


Last update:Jun 27, 2022 at 12:58:02