14.5.7.3.1 Recurrent Neural Networks for Shapes and Complex Features, RNN

Chapter Contents (Back)
Feature Description. Recurrent Neural Networks. RNN. A subset.

Varoglu, E., Hacioglu, K.,
Recurrent neural network speech predictor based on dynamical systems approach,
VISP(147), No. 2, April 2000, pp. 149. 0005
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Gupta, L.[Lalit], McAvoy, M.[Mark],
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Classification of temporal sequences via prediction using the simple recurrent neural network,
PR(33), No. 10, October 2000, pp. 1759-1770.
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Model dimension/order determination; Nonlinear system identification; Recurrent neural networks; Minimal realization BibRef

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Copula Echo state networks for Recurrent NN training. BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, G.[Gang],
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling,
SPLetters(22), No. 11, November 2015, pp. 1990-1994.
IEEE DOI 1509
feature extraction See also Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification. BibRef

Zuo, Z.[Zhen], Shuai, B.[Bing], Wang, G.[Gang], Liu, X.[Xiao], Wang, X.X.[Xing-Xing], Wang, B.[Bing], Chen, Y.S.[Yu-Shi],
Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks,
IP(25), No. 7, July 2016, pp. 2983-2996.
IEEE DOI 1606
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Earlier:
Convolutional recurrent neural networks: Learning spatial dependencies for image representation,
DeepLearn15(18-26)
IEEE DOI 1510
computational complexity. Computational modeling BibRef

Shuai, B.[Bing], Zuo, Z.[Zhen], Wang, B.[Bing], Wang, G.[Gang],
Scene Segmentation with DAG-Recurrent Neural Networks,
PAMI(40), No. 6, June 2018, pp. 1480-1493.
IEEE DOI 1805
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Earlier:
DAG-Recurrent Neural Networks for Scene Labeling,
CVPR16(3620-3629)
IEEE DOI 1612
Context, Context modeling, Image segmentation, Neural networks, Object segmentation, Semantics, Training, CNN, COCO stuff, DAG-RNN, sift flow BibRef

Abdulnabi, A.H., Shuai, B., Zuo, Z., Chau, L.P., Wang, G.,
Multimodal Recurrent Neural Networks With Information Transfer Layers for Indoor Scene Labeling,
MultMed(20), No. 7, July 2018, pp. 1656-1671.
IEEE DOI 1806
Adaptation models, Context modeling, Feature extraction, Kernel, Labeling, Recurrent neural networks, CNNs, Multimodal learning, RNNs BibRef

Lakhal, M.I.[Mohamed Ilyes], Çevikalp, H.[Hakan], Escalera, S.[Sergio], Ofli, F.[Ferda],
Recurrent neural networks for remote sensing image classification,
IET-CV(12), No. 7, October 2018, pp. 1040-1045.
DOI Link 1809
See also Residual Stacked RNNs for Action Recognition. BibRef

Han, Z., Shang, M., Liu, Z., Vong, C., Liu, Y., Zwicker, M., Han, J., Chen, C.L.P.,
SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention,
IP(28), No. 2, February 2019, pp. 658-672.
IEEE DOI 1811
feature extraction, image classification, learning (artificial intelligence), recurrent neural nets, attention BibRef

Han, Z., Lu, H., Liu, Z., Vong, C., Liu, Y., Zwicker, M., Han, J., Chen, C.L.P.,
3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN With Hierarchical Attention Aggregation,
IP(28), No. 8, August 2019, pp. 3986-3999.
IEEE DOI 1907
convolutional neural nets, feature extraction, image classification, image representation, image retrieval, CNN BibRef

Godin, F.[Fréderic], Degrave, J.[Jonas], Dambre, J.[Joni], De Neve, W.[Wesley],
Dual Rectified Linear Units (DReLUs): A replacement for tanh activation functions in Quasi-Recurrent Neural Networks,
PRL(116), 2018, pp. 8-14.
Elsevier DOI 1812
Activation functions, ReLU, Dual Rectified Linear Unit, Recurrent Neural Networks, Language modeling BibRef

Song, A.[Ahram], Choi, J.[Jaewan], Han, Y.K.[You-Kyung], Kim, Y.[Yongil],
Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Wang, Q.[Qi], Liu, S.T.[Shao-Teng], Chanussot, J.[Jocelyn], Li, X.L.[Xue-Long],
Scene Classification With Recurrent Attention of VHR Remote Sensing Images,
GeoRS(57), No. 2, February 2019, pp. 1155-1167.
IEEE DOI 1901
Remote sensing, Feature extraction, Training, Saliency detection, Machine learning, Task analysis, Attention, scene classification BibRef

Zia, T.[Tehseen],
Hierarchical recurrent highway networks,
PRL(119), 2019, pp. 71-76.
Elsevier DOI 1902
Recurrent neural networks, Sequence modeling, Highway networks, Language modeling BibRef

Kinghorn, P.[Philip], Zhang, L.[Li], Shao, L.[Ling],
A hierarchical and regional deep learning architecture for image description generation,
PRL(119), 2019, pp. 77-85.
Elsevier DOI 1902
Image captioning, Deep Neural Networks, Recurrent Neural Networks, Region Annotation BibRef

Ma, A.[Andong], Filippi, A.M.[Anthony M.], Wang, Z.Y.[Zhang-Yang], Yin, Z.C.[Zheng-Cong],
Hyperspectral Image Classification Using Similarity Measurements-Based Deep Recurrent Neural Networks,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
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Becker, S.[Stefan], Hug, R.[Ronny], Hübner, W.[Wolfgang], Arens, M.[Michael],
An RNN-Based IMM Filter Surrogate,
SCIA19(387-398).
Springer DOI 1906
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Vinayavekhin, P., Chaudhury, S., Munawar, A., Agravante, D.J., De Magistris, G., Kimura, D., Tachibana, R.,
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention,
ICPR18(2624-2629)
IEEE DOI 1812
Task analysis, Data models, Data visualization, Recurrent neural networks, Decoding BibRef

You, Q., Luo, J., Zhang, Z.,
End-to-End Convolutional Semantic Embeddings,
CVPR18(5735-5744)
IEEE DOI 1812
Semantics, Visualization, Task analysis, Convolutional neural networks, Recurrent neural networks, Computational modeling BibRef

Ye, J., Wang, L., Li, G., Chen, D., Zhe, S., Chu, X., Xu, Z.,
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition,
CVPR18(9378-9387)
IEEE DOI 1812
Computational modeling, Data models, Recurrent neural networks, Computer architecture, Correlation, Matrix decomposition BibRef

Lambert, J., Sener, O., Savarese, S.,
Deep Learning Under Privileged Information Using Heteroscedastic Dropout,
CVPR18(8886-8895)
IEEE DOI 1812
Training, Tools, Task analysis, Support vector machines, Machine learning, Recurrent neural networks 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

Choo, S., Seo, W., Jeong, D., Cho, N.I.,
Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification,
ICPR18(103-108)
IEEE DOI 1812
Training, Video sequences, Decoding, Computer architecture, Benchmark testing, Semantics, Image segmentation BibRef

Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.,
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN,
CVPR18(5457-5466)
IEEE DOI 1812
Neurons, Recurrent neural networks, Training, Logic gates, Backpropagation, Eigenvalues and eigenfunctions, Task analysis BibRef

Ye, Z., Du, Y., Wu, F.,
Graph-based Semi-supervised Classification with CRF and RNN,
ICPR18(403-408)
IEEE DOI 1812
Labeling, Task analysis, Approximation algorithms, Inference algorithms, Logic gates, Computer architecture, Feature extraction BibRef

Bargal, S.A., Zunino, A., Kim, D., Zhang, J., Murino, V., Sclaroff, S.,
Excitation Backprop for RNNs,
CVPR18(1440-1449)
IEEE DOI 1812
Neurons, Task analysis, Spatiotemporal phenomena, Grounding, Visualization, Computational modeling, Standards BibRef

Acuna, D., Ling, H., Kar, A., Fidler, S.,
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++,
CVPR18(859-868)
IEEE DOI 1812
Training, Decoding, Predictive models, Neural networks, Labeling, Computer architecture BibRef

Oliu, M.[Marc], Selva, J.[Javier], Escalera, S.[Sergio],
Folded Recurrent Neural Networks for Future Video Prediction,
ECCV18(XIV: 745-761).
Springer DOI 1810
BibRef

Ye, X.Q.[Xiao-Qing], Li, J.[Jiamao], Huang, H.[Hexiao], Du, L.[Liang], Zhang, X.L.[Xiao-Lin],
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation,
ECCV18(VII: 415-430).
Springer DOI 1810
BibRef

Sadeghian, A.[Amir], Legros, F.[Ferdinand], Voisin, M.[Maxime], Vesel, R.[Ricky], Alahi, A.[Alexandre], Savarese, S.[Silvio],
CAR-Net: Clairvoyant Attentive Recurrent Network,
ECCV18(XI: 162-180).
Springer DOI 1810
BibRef

Raue, F.[Federico], Byeon, W.[Wonmin], Breuel, T.M.[Thomas M.], Liwicki, M.[Marcus],
Parallel sequence classification using recurrent neural networks and alignment,
ICDAR15(581-585)
IEEE DOI 1511
BibRef

Tavakoli, H.R.[Hamed R.], Borji, A.[Ali], Anwer, R.M.[Rao Muhammad], Rahtu, E.[Esa], Kannala, J.[Juho],
Bottom-Up Attention Guidance for Recurrent Image Recognition,
ICIP18(3004-3008)
IEEE DOI 1809
Computational modeling, Task analysis, Computer architecture, Image recognition, Predictive models, Pipelines, deep neural networks BibRef

Zhao, Z., Wu, X., Chen, P.C.Y., Chen, W.,
General Recurrent Attention Model for Jointly Multiple Object Recognition and Weakly Supervised Localization,
ICIP18(341-345)
IEEE DOI 1809
Erbium, Indexes, Attention, Recognition, Localization, Reinforcement Learning BibRef

Wang, Q., Li, P.,
D-LSM: Deep Liquid State Machine with unsupervised recurrent reservoir tuning,
ICPR16(2652-2657)
IEEE DOI 1705
Biological neural networks, Convolution, Feature extraction, Kernel, Liquids, Neurons, Reservoirs BibRef

Gandhi, A.[Ankit], Sharma, A.[Arjun], Biswas, A.[Arijit], Deshmukh, O.[Om],
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion,
CVAVM16(II: 883-899).
Springer DOI 1611
BibRef

Luo, W.X.[Wei-Xin], Liu, W.[Wen], Gao, S.H.[Sheng-Hua],
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework,
ICCV17(341-349)
IEEE DOI 1802
compressed sensing, feature extraction, image reconstruction, learning (artificial intelligence), recurrent neural nets, Training BibRef

Aviles, A.I., Marban, A., Sobrevilla, P., Fernandez, J., Casals, A.,
A recurrent neural network approach for 3D vision-based force estimation,
IPTA14(1-6)
IEEE DOI 1503
dexterous manipulators BibRef

Hillar, C.[Christopher], Mehta, R.[Ram], Koepsell, K.[Kilian],
A hopfield recurrent neural network trained on natural images performs state-of-the-art image compression,
ICIP14(4092-4096)
IEEE DOI 1502
Image coding BibRef

Prokhorov, D.V.[Danil V.],
Object recognition in 3D lidar data with recurrent neural network,
OTCBVS09(9-15).
IEEE DOI 0906
BibRef

Chen, J.M.[Jin-Miao], Chaudhari, N.S.,
Improvement of bidirectional recurrent neural network for learning long-term dependencies,
ICPR04(IV: 593-596).
IEEE DOI 0409
BibRef

Morita, S.[Satoru],
Learning Behavior Using Multiresolution Recurrent Neural Network,
CAIP99(157-166).
Springer DOI 9909
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Song, H.H., Lee, S.W.,
A New Recurrent Neural Network Architecture for Pattern Recognition,
ICPR96(IV: 718-722).
IEEE DOI 9608
(Korea Univ., KOR) BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Residual Neural Networks, ResNet .


Last update:Aug 10, 2019 at 15:07:15