Varoglu, E.,
Hacioglu, K.,
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Elsevier DOI
0008
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Gupta, L.[Lalit],
McAvoy, M.[Mark],
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Elsevier DOI
0006
BibRef
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A multiobjective genetic algorithm for obtaining the optimal size of a
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Elsevier DOI
0506
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Model dimension/order determination; Nonlinear system identification;
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The copula echo state network,
PR(45), No. 1, 2012, pp. 570-577.
Elsevier DOI
1410
Copula
Echo state networks for Recurrent NN training.
BibRef
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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
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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
BibRef
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
BibRef
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
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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
Bu, S.H.[Shu-Hui],
Han, P.C.[Peng-Cheng],
Liu, Z.B.[Zhen-Bao],
Han, J.W.[Jun-Wei],
Scene parsing using inference Embedded Deep Networks,
PR(59), No. 1, 2016, pp. 188-198.
Elsevier DOI
1609
Convolutional Neural Networks (CNNs)
BibRef
Liu, N.[Nian],
Han, J.W.[Jun-Wei],
A Deep Spatial Contextual Long-Term Recurrent Convolutional Network
for Saliency Detection,
IP(27), No. 7, July 2018, pp. 3264-3274.
IEEE DOI
1805
BibRef
Earlier:
DHSNet: Deep Hierarchical Saliency Network for Salient Object
Detection,
CVPR16(678-686)
IEEE DOI
1612
feature extraction, learning (artificial intelligence),
recurrent neural nets, DSCLRCN, DSCLSTM model,
scene context
BibRef
Maggiori, E.,
Tarabalka, Y.,
Charpiat, G.,
Alliez, P.,
Convolutional Neural Networks for Large-Scale Remote-Sensing Image
Classification,
GeoRS(55), No. 2, February 2017, pp. 645-657.
IEEE DOI
1702
geophysical image processing
BibRef
Maggiori, E.,
Charpiat, G.,
Tarabalka, Y.,
Alliez, P.,
Recurrent Neural Networks to Correct Satellite Image Classification
Maps,
GeoRS(55), No. 9, September 2017, pp. 4962-4971.
IEEE DOI
1709
image classification, neural nets,
aerial image labeling, convolutional neural networks,
generic iterative enhancement process,
iterative process, partial differential equations,
pixelwise semantic labeling,
satellite image classification maps,
BibRef
Maggiori, E.,
Tarabalka, Y.,
Charpiat, G.,
Alliez, P.,
High-Resolution Aerial Image Labeling With Convolutional Neural
Networks,
GeoRS(55), No. 12, December 2017, pp. 7092-7103.
IEEE DOI
1712
Convolutional codes, Image analysis,
Labeling, Neurons, Semantics, Convolutional neural networks (CNNs),
semantic labeling
BibRef
Donahue, J.[Jeff],
Hendricks, L.A.[Lisa Anne],
Rohrbach, M.[Marcus],
Venugopalan, S.[Subhashini],
Guadarrama, S.[Sergio],
Saenko, K.[Kate],
Darrell, T.J.[Trevor J.],
Long-Term Recurrent Convolutional Networks for Visual Recognition and
Description,
PAMI(39), No. 4, April 2017, pp. 677-691.
IEEE DOI
1703
BibRef
Earlier: A1, A2, A5, A3, A4, A7, A6:
CVPR15(2625-2634)
IEEE DOI
1510
Computational modeling
BibRef
Hendricks, L.A.[Lisa Anne],
Hu, R.H.[Rong-Hang],
Darrell, T.J.[Trevor J.],
Akata, Z.[Zeynep],
Grounding Visual Explanations,
ECCV18(II: 269-286).
Springer DOI
1810
BibRef
Hendricks, L.A.[Lisa Anne],
Akata, Z.[Zeynep],
Rohrbach, M.[Marcus],
Donahue, J.[Jeff],
Schiele, B.[Bernt],
Darrell, T.J.[Trevor J.],
Generating Visual Explanations,
ECCV16(IV: 3-19).
Springer DOI
1611
Why the classification.
BibRef
Wu, H.[Hao],
Prasad, S.[Saurabh],
Convolutional Recurrent Neural Networks for Hyperspectral Data
Classification,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link
1704
BibRef
Mhalla, A.[Ala],
Chateau, T.[Thierry],
Maâmatou, H.[Houda],
Gazzah, S.[Sami],
Ben Amara, N.E.[Najoua Essoukri],
SMC faster R-CNN: Toward a scene-specialized multi-object detector,
CVIU(164), No. 1, 2017, pp. 3-15.
Elsevier DOI
1801
BibRef
Earlier: A1, A3, A2, A4, A5:
Faster R-CNN Scene Specialization with a Sequential Monte-Carlo
Framework,
DICTA16(1-7)
IEEE DOI
1701
Transfer learning
Approximation algorithms
BibRef
Zimmermann, R.S.[Roland S.],
Siems, J.N.[Julien N.],
Faster training of Mask R-CNN by focusing on instance boundaries,
CVIU(188), 2019, pp. 102795.
Elsevier DOI
1910
Mask R-CNN, Instance segmentation,
Auxiliary task, Edge detection filter, Sobel filter,
Convolutional neural network
BibRef
Lu, W.,
Cheng, Y.,
Xiao, C.,
Chang, S.,
Huang, S.,
Liang, B.,
Huang, T.,
Unsupervised Sequential Outlier Detection With Deep Architectures,
IP(26), No. 9, September 2017, pp. 4321-4330.
IEEE DOI
1708
correlation methods, object detection, recurrent neural nets,
applications domains, autoencoder models, critical systems,
BibRef
Bergado, J.R.,
Persello, C.,
Stein, A.,
Recurrent Multiresolution Convolutional Networks for VHR Image
Classification,
GeoRS(56), No. 11, November 2018, pp. 6361-6374.
IEEE DOI
1811
Feature extraction, Spatial resolution, Image fusion,
Task analysis, Kernel, Labeling, Convolutional networks,
very high-resolution (VHR) image
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
BibRef
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
Shen, J.[Junge],
Yu, T.W.[Tian-Wei],
Yang, H.P.[Hao-Peng],
Wang, R.[Ruxin],
Wang, Q.[Qi],
An Attention Cascade Global-Local Network for Remote Sensing
Scene Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Shen, J.[Junge],
Zhang, T.[Tong],
Wang, Y.C.[Yi-Chen],
Wang, R.[Ruxin],
Wang, Q.[Qi],
Qi, M.[Min],
A Dual-Model Architecture with Grouping-Attention-Fusion for Remote
Sensing Scene Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
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.D.[An-Dong],
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
BibRef
Ma, A.D.[An-Dong],
Filippi, A.M.[Anthony M.],
Wang, Z.Y.[Zhang-Yang],
Yin, Z.C.[Zheng-Cong],
Huo, D.[Da],
Li, X.[Xiao],
Güneralp, B.[Burak],
Fast Sequential Feature Extraction for Recurrent Neural Network-Based
Hyperspectral Image Classification,
GeoRS(59), No. 7, July 2021, pp. 5920-5937.
IEEE DOI
2106
Feature extraction, Training, Image segmentation,
Recurrent neural networks, Hyperspectral imaging,
similarity measurements
BibRef
Liu, Y.C.[Yuan-Chao],
Wang, J.Q.[Jun-Qi],
Wang, X.L.[Xiao-Long],
Learning to recognize opinion targets using recurrent neural networks,
PRL(106), 2018, pp. 41-46.
Elsevier DOI
1804
Opinion target extraction, Sentiment analysis, Customer review,
mining, Neural networks
BibRef
Dong, R.,
Xu, D.,
Zhao, J.,
Jiao, L.,
An, J.,
Sig-NMS-Based Faster R-CNN Combining Transfer Learning for Small
Target Detection in VHR Optical Remote Sensing Imagery,
GeoRS(57), No. 11, November 2019, pp. 8534-8545.
IEEE DOI
1911
Optical imaging, Optical sensors, Remote sensing, Object detection,
Optical fiber networks, Task analysis,
transfer learning
BibRef
Zhang, P.,
Xue, J.,
Lan, C.,
Zeng, W.,
Gao, Z.,
Zheng, N.,
EleAtt-RNN:
Adding Attentiveness to Neurons in Recurrent Neural Networks,
IP(29), 2020, pp. 1061-1073.
IEEE DOI
1911
Logic gates, Neurons, Task analysis, Standards, Gesture recognition,
Recurrent neural networks, Videos,
gesture recognition
BibRef
Ren, S.Q.[Shao-Qing],
He, K.M.[Kai-Ming],
Girshick, R.[Ross],
Sun, J.[Jian],
Faster R-CNN:
Towards Real-Time Object Detection with Region Proposal
Networks,
PAMI(39), No. 6, June 2017, pp. 1137-1149.
IEEE DOI
1705
Convolutional codes, Detectors, Feature extraction,
Object detection, Proposals, Search problems, Training,
convolutional neural network, region proposal.
BibRef
Qin, Z.[Zheng],
Li, Z.M.[Ze-Ming],
Zhang, Z.N.[Zhao-Ning],
Bao, Y.P.[Yi-Ping],
Yu, G.[Gang],
Peng, Y.X.[Yu-Xing],
Sun, J.[Jian],
ThunderNet: Towards Real-Time Generic Object Detection on Mobile
Devices,
ICCV19(6717-6726)
IEEE DOI
2004
image representation, mobile computing,
object detection, ThunderNet, real-time generic object detection,
Computational efficiency
BibRef
He, K.M.[Kai-Ming],
Gkioxari, G.,
Dollár, P.,
Girshick, R.,
Mask R-CNN,
PAMI(42), No. 2, February 2020, pp. 386-397.
IEEE DOI
2001
BibRef
Earlier:
ICCV17(2980-2988)
IEEE DOI
1802
Award, Marr Prize. Task analysis, Semantics, Feature extraction, Object detection,
Proposals, Image segmentation, Quantization (signal),
convolutional neural network.
feature extraction,
pose estimation, Faster R-CNN, bounding-box object detection,
Semantics
BibRef
Girshick, R.,
Fast R-CNN,
ICCV15(1440-1448)
IEEE DOI
1602
Computer architecture
BibRef
Ren, Y.[Yun],
Zhu, C.R.[Chang-Ren],
Xiao, S.P.[Shun-Ping],
Deformable Faster R-CNN with Aggregating Multi-Layer Features for
Partially Occluded Object Detection in Optical Remote Sensing Images,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link
1810
BibRef
Yan, J.Q.[Jiang-Qiao],
Wang, H.Q.[Hong-Qi],
Yan, M.L.[Meng-Long],
Diao, W.H.[Wen-Hui],
Sun, X.[Xian],
Li, H.[Hao],
IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class
Object Detection in Remote Sensing Imagery,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link
1902
BibRef
Sun, Y.X.[Yu-Xi],
Amano, H.[Hideharu],
FiC-RNN: A Multi-FPGA Acceleration Framework for Deep Recurrent Neural
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IEICE(E103-D), No. 12, December 2020, pp. 2457-2462.
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Zhu, L.[Li],
Xie, Z.H.[Zi-Hao],
Liu, L.M.[Li-Man],
Tao, B.[Bo],
Tao, W.B.[Wen-Bing],
IoU-uniform R-CNN: Breaking through the limitations of RPN,
PR(112), 2021, pp. 107816.
Elsevier DOI
2102
Object detection, Two-stage detector, RPN, IoU distribution imbalance
BibRef
Hao, S.,
Wang, W.,
Salzmann, M.,
Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image
Classification,
GeoRS(59), No. 3, March 2021, pp. 2448-2460.
IEEE DOI
2103
Logic gates, Recurrent neural networks, Feature extraction,
Geometry, Hyperspectral imaging, Deep learning,
U-shaped network (U-Net)
BibRef
Chen, Y.H.[Yu-Hua],
Wang, H.R.[Hao-Ran],
Li, W.[Wen],
Sakaridis, C.[Christos],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Scale-Aware Domain Adaptive Faster R-CNN,
IJCV(129), No. 7, July 2021, pp. 2223-2243.
Springer DOI
2106
BibRef
Earlier: A1, A3, A4, A5, A6, Only:
Domain Adaptive Faster R-CNN for Object Detection in the Wild,
CVPR18(3339-3348)
IEEE DOI
1812
Object detection, Training, Adaptation models, Proposals,
Task analysis, Lighting, Feature extraction
BibRef
Liang, L.[Le],
Wang, G.[Guoli],
Efficient recurrent attention network for remote sensing scene
classification,
IET-IPR(15), No. 8, 2021, pp. 1712-1721.
DOI Link
2106
BibRef
Garner, P.N.[Philip N.],
Tong, S.[Sibo],
A Bayesian Approach to Recurrence in Neural Networks,
PAMI(43), No. 8, August 2021, pp. 2527-2537.
IEEE DOI
2107
Logic gates, Probabilistic logic, Bayes methods,
Signal processing algorithms, Hidden Markov models, Training,
bidirectional LSTM
BibRef
Gammelli, D.[Daniele],
Rodrigues, F.[Filipe],
Recurrent flow networks: A recurrent latent variable model for
density estimation of urban mobility,
PR(129), 2022, pp. 108752.
Elsevier DOI
2206
Urban mobility, Latent variable models, Normalizing flows, Variational inference
BibRef
Turkoglu, M.O.[Mehmet Ozgur],
d'Aronco, S.[Stefano],
Wegner, J.D.[Jan Dirk],
Schindler, K.[Konrad],
Gating Revisited: Deep Multi-Layer RNNs That can be Trained,
PAMI(44), No. 8, August 2022, pp. 4081-4092.
IEEE DOI
2207
Microprocessors, Training, Logic gates,
Recurrent neural networks, Task analysis, Lattices,
multi-layer RNN
BibRef
Pei, W.J.[Wen-Jie],
Feng, X.[Xin],
Fu, C.M.[Can-Miao],
Cao, Q.[Qiong],
Lu, G.M.[Guang-Ming],
Tai, Y.W.[Yu-Wing],
Learning Sequence Representations by Non-local Recurrent Neural Memory,
IJCV(130), No. 10, October 2022, pp. 2532-2552.
Springer DOI
2209
BibRef
Fu, C.M.[Can-Miao],
Pei, W.J.[Wen-Jie],
Cao, Q.[Qiong],
Zhang, C.P.[Chao-Peng],
Zhao, Y.[Yong],
Shen, X.Y.[Xiao-Yong],
Tai, Y.W.[Yu-Wing],
Non-Local Recurrent Neural Memory for Supervised Sequence Modeling,
ICCV19(6310-6319)
IEEE DOI
2004
image sequences, learning (artificial intelligence),
recurrent neural nets, sentiment analysis, long-range dependencies
BibRef
Cheng, Z.H.[Zi-Heng],
Chen, B.[Bo],
Lu, R.Y.[Rui-Ying],
Wang, Z.J.[Zheng-Jue],
Zhang, H.[Hao],
Meng, Z.Y.[Zi-Yi],
Yuan, X.[Xin],
Recurrent Neural Networks for Snapshot Compressive Imaging,
PAMI(45), No. 2, February 2023, pp. 2264-2281.
IEEE DOI
2301
BibRef
Earlier: A1, A3, A4, A5, A2, A6, A7:
Birnat: Bidirectional Recurrent Neural Networks with Adversarial
Training for Video Snapshot Compressive Imaging,
ECCV20(XXIV:258-275).
Springer DOI
2012
Videos, Image reconstruction, Apertures, Image color analysis,
Image coding, Cameras, Deep learning, Snapshot compressive imaging,
coded aperture snapshot spectral imaging (CASSI)
BibRef
Meng, Z.Y.[Zi-Yi],
Yu, Z.M.[Zhen-Ming],
Xu, K.[Kun],
Yuan, X.[Xin],
Self-supervised Neural Networks for Spectral Snapshot Compressive
Imaging,
ICCV21(2602-2611)
IEEE DOI
2203
Training, Image coding, Neural networks, Noise reduction, Imaging,
Training data, Image reconstruction, Computational photography,
Image and video retrieval
BibRef
Meng, Z.Y.[Zi-Yi],
Yuan, X.[Xin],
Perception Inspired Deep Neural Networks for Spectral Snapshot
Compressive Imaging,
ICIP21(2813-2817)
IEEE DOI
2201
Deep learning, Image quality, Image coding, Imaging, Apertures, Data models,
Compressive sensing, spectral compressive imaging, perceptual loss
BibRef
Wang, Y.D.[Ya-Dong],
Bai, X.Z.[Xiang-Zhi],
Versatile recurrent neural network for wide types of video
restoration,
PR(138), 2023, pp. 109360.
Elsevier DOI
2303
RNN, Video restoration, Versatile, Efficient
BibRef
Wang, C.[Cheng],
Lawrence, C.[Carolin],
Niepert, M.[Mathias],
State-Regularized Recurrent Neural Networks to Extract Automata and
Explain Predictions,
PAMI(45), No. 6, June 2023, pp. 7739-7750.
IEEE DOI
2305
Stochastic processes, Logic gates, Learning automata,
Behavioral sciences, Probabilistic logic, Symbols, state machine
BibRef
Yao, Z.Y.[Zhi-Yu],
Wang, Y.[Yunbo],
Wu, H.[Haixu],
Wang, J.M.[Jian-Min],
Long, M.S.[Ming-Sheng],
ModeRNN: Harnessing Spatiotemporal Mode Collapse in Unsupervised
Predictive Learning,
PAMI(45), No. 11, November 2023, pp. 13281-13296.
IEEE DOI
2310
BibRef
Ji, Z.Y.[Zhen-Yan],
Song, X.J.[Xiao-Jun],
Feng, Q.[Qibo],
Wang, H.S.[Hai-Shuai],
Chen, C.H.[Chi-Hua],
Chang, C.C.[Chin-Chen],
RSG-Net: A Recurrent Similarity Network With Ghost Convolution for
Wheelset Laser Stripe Image Inpainting,
ITS(24), No. 11, November 2023, pp. 12852-12861.
IEEE DOI
2311
BibRef
Xie, X.X.[Xing-Xing],
Cheng, G.[Gong],
Wang, J.B.[Jia-Bao],
Li, K.[Ke],
Yao, X.W.[Xi-Wen],
Han, J.W.[Jun-Wei],
Oriented R-CNN and Beyond,
IJCV(132), No. 7, July 2024, pp. Pages2420-2442.
Springer DOI
2406
BibRef
Thawakar, O.[Omkar],
Rivkind, A.[Alexandre],
Ahissar, E.[Ehud],
Khan, F.S.[Fahad Shahbaz],
Fast Video Instance Segmentation via Recurrent Encoder-based
Transformers,
CAIP23(I:262-272).
Springer DOI
2312
BibRef
Gazzeh, S.[Soulayma],
Lo Presti, L.[Liliana],
Douik, A.[Ali],
La Cascia, M.[Marco],
Rlstm: A Novel Residual and Recurrent Network for Pedestrian Action
Classification,
CAIP23(II:55-64).
Springer DOI
2312
BibRef
Keddous, F.E.[Fekhr Eddine],
Shvai, N.[Nadiya],
Llanza, A.[Arcadi],
Nakib, A.[Amir],
Inference Acceleration of Deep Learning Classifiers Based on RNN,
ICIP23(2450-2454)
IEEE DOI
2312
BibRef
Wang, J.Y.[Jia-Yun],
Chen, Y.[Yubei],
Yu, S.X.[Stella X.],
Cheung, B.[Brian],
Le Cun, Y.L.[Yann L.],
Compact and Optimal Deep Learning with Recurrent Parameter Generators,
WACV23(3889-3899)
IEEE DOI
2302
Deep learning, Training, Performance gain, Generators, Decorrelation,
Algorithms: Machine learning architectures, formulations,
algorithms (including transfer)
BibRef
Wu, G.J.[Guo-Jun],
Zhang, X.[Xin],
Zhang, Z.M.[Zi-Ming],
Li, Y.H.[Yan-Hua],
Zhou, X.[Xun],
Brinton, C.[Christopher],
Liu, Z.M.[Zhen-Ming],
Learning Lightweight Neural Networks via Channel-Split Recurrent
Convolution,
WACV23(3847-3857)
IEEE DOI
2302
Convolutional codes, Training, Image coding, Embedded systems,
Convolution, Neural networks, and algorithms (including transfer)
BibRef
Guan, X.S.[Xiang-Shuo],
Wang, D.[Dan],
Xiong, C.C.[Cong-Cong],
Li, S.R.[Shang-Rong],
Chen, Y.N.[You-Ning],
PBGAN: Path Based Graph Attention Network for Heterophily,
ICPR22(2199-2206)
IEEE DOI
2212
Learning systems, Recurrent neural networks,
Computational modeling, Graph neural networks, Encoding,
Recurrent Neural Networks
BibRef
Motallebi, M.R.[Mohammad Reza],
Westfechtel, T.[Thomas],
Li, Y.[Yang],
Harada, T.[Tatsuya],
Unsupervised Hierarchical Disentanglement for Video Prediction,
ICPR22(470-476)
IEEE DOI
2212
Visualization, Recurrent neural networks, Predictive models,
Feature extraction, Graph neural networks, Object tracking
BibRef
Becker, S.[Stefan],
Hug, R.[Ronny],
Huebner, W.[Wolfgang],
Arens, M.[Michael],
Morris, B.T.[Brendan T.],
Handling Missing Observations with an RNN-based Prediction-Update Cycle,
CAIP21(I:311-321).
Springer DOI
2112
BibRef
Kag, A.[Anil],
Saligrama, V.[Venkatesh],
Time Adaptive Recurrent Neural Network,
CVPR21(15144-15153)
IEEE DOI
2111
Training, Learning systems, Recurrent neural networks,
Adaptive systems, Memory management, Benchmark testing, Propagation losses
BibRef
Yin, M.[Miao],
Liao, S.[Siyu],
Liu, X.Y.[Xiao-Yang],
Wang, X.D.[Xiao-Dong],
Yuan, B.[Bo],
Towards Extremely Compact RNNs for Video Recognition with Fully
Decomposed Hierarchical Tucker Structure,
CVPR21(12080-12089)
IEEE DOI
2111
Analytical models, Tensors,
Recurrent neural networks, Sequences, Costs, Computational modeling
BibRef
Srivastava, A.[Ayush],
Dutta, O.[Oshin],
Gupta, J.[Jigyasa],
Agarwal, S.[Sumeet],
Prathosh, A.P.,
A Variational Information Bottleneck Based Method to Compress
Sequential Networks for Human Action Recognition,
WACV21(2744-2753)
IEEE DOI
2106
Performance evaluation,
Recurrent neural networks, Computational modeling, Feature extraction
BibRef
Guo, R.[Ruohao],
Learning Stable Deep Predictive Coding Networks with Weight Norm
Supervision,
ICPR21(10600-10607)
IEEE DOI
2105
Similar structure to recurrent NN.
Training, Visualization, Sufficient conditions,
Recurrent neural networks, Heuristic algorithms,
Prediction algorithms
BibRef
Yue, Y.[Yun],
Li, M.[Ming],
Saligrama, V.[Venkatesh],
Zhang, Z.M.[Zi-Ming],
RNN Training along Locally Optimal Trajectories via Frank-Wolfe
Algorithm,
ICPR21(10532-10539)
IEEE DOI
2105
Training, Backpropagation, Benchmark testing, Trajectory,
Noise measurement, Convergence
BibRef
Zhou, W.[Weilian],
Sei-Ichiro Kamata,
Multi-Scanning Based Recurrent Neural Network for Hyperspectral Image
Classification,
ICPR21(4743-4750)
IEEE DOI
2105
Deep learning, Recurrent neural networks,
Benchmark testing,
hyperspectral image classification
BibRef
Aksasse, H.,
Aksasse, B.,
Ouanan, M.,
Developing Good Habits Using Deep Learning Techniques,
ISCV20(1-5)
IEEE DOI
2011
convolutional neural nets, learning (artificial intelligence),
recurrent neural nets, supervised learning, deep learning,
activity classification
BibRef
Wang, T.,
Huang, J.,
Zhang, H.,
Sun, Q.,
Visual Commonsense R-CNN,
CVPR20(10757-10767)
IEEE DOI
2008
Visualization, Dogs, Legged locomotion, Task analysis,
Feature extraction, Correlation, Learning systems
BibRef
Beery, S.,
Wu, G.,
Rathod, V.,
Votel, R.,
Huang, J.,
Context R-CNN: Long Term Temporal Context for Per-Camera Object
Detection,
CVPR20(13072-13082)
IEEE DOI
2008
Cameras, Monitoring, Feature extraction, Object detection,
Context modeling, Convolution
BibRef
Castrejon, L.[Lluis],
Ballas, N.[Nicolas],
Courville, A.[Aaron],
Cascaded Video Generation for Videos In-the-Wild,
ICPR22(2385-2392)
IEEE DOI
2212
BibRef
Earlier:
Improved Conditional VRNNs for Video Prediction,
ICCV19(7607-7616)
IEEE DOI
2004
Computational modeling, Generators, Computational complexity, Videos.
Bayes methods, image sequences, recurrent neural nets,
statistical distributions, video signal processing, Measurement
BibRef
Wang, Z.,
Zou, W.,
Xu, C.,
PR Product: A Substitute for Inner Product in Neural Networks,
ICCV19(6012-6021)
IEEE DOI
2004
Code, Neural Netowrks.
WWW Link. convolutional neural nets, image classification,
learning (artificial intelligence), recurrent neural nets,
Computational modeling
BibRef
Wang, J.[Jiasi],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning,
ICPR18(2416-2421)
IEEE DOI
1812
Proposals, Detectors, Training, Feature extraction, Object detection,
Labeling, Complexity theory
BibRef
Burlina, P.,
MRCNN: A stateful Fast R-CNN,
ICPR16(3518-3523)
IEEE DOI
1705
Bayes methods, Filtering, Graphics processing units,
Object detection, Proposals, Target tracking, ConvNets,
Deep Learning, Fast R-CNN, Region CNNs in Video, Region, Proposals
BibRef
Shrivastava, A.[Abhinav],
Gupta, A.[Abhinav],
Contextual Priming and Feedback for Faster R-CNN,
ECCV16(I: 330-348).
Springer DOI
1611
BibRef
Deza, A.[Arturo],
Surana, A.[Amit],
Eckstein, M.P.[Miguel P.],
Assessment of Faster R-CNN in Man-Machine Collaborative Search,
CVPR19(3180-3189).
IEEE DOI
2002
BibRef
Hinami, R.[Ryota],
Satoh, S.[Shin'ichi],
Large-Scale R-CNN with Classifier Adaptive Quantization,
ECCV16(III: 403-419).
Springer DOI
1611
BibRef
Huang, Z.J.[Zhao-Jin],
Huang, L.C.[Li-Chao],
Gong, Y.C.[Yong-Chao],
Huang, C.[Chang],
Wang, X.G.[Xing-Gang],
Mask Scoring R-CNN,
CVPR19(6402-6411).
IEEE DOI
2002
BibRef
Lu, X.[Xin],
Li, B.[Buyu],
Yue, Y.X.[Yu-Xin],
Li, Q.Q.[Quan-Quan],
Yan, J.J.[Jun-Jie],
Grid R-CNN,
CVPR19(7355-7364).
IEEE DOI
2002
BibRef
Pang, B.[Bo],
Zha, K.W.[Kai-Wen],
Cao, H.[Hanwen],
Shi, C.[Chen],
Lu, C.[Cewu],
Deep RNN Framework for Visual Sequential Applications,
CVPR19(423-432).
IEEE DOI
2002
BibRef
Tung, H.Y.F.[Hsiao-Yu Fish],
Cheng, R.[Ricson],
Fragkiadaki, K.[Katerina],
Learning Spatial Common Sense With Geometry-Aware Recurrent Networks,
CVPR19(2590-2598).
IEEE DOI
2002
BibRef
Wang, Y.,
Li, D.,
Lin, L.,
Chen, B.,
Wang, L.,
Zhang, M.,
Feature Aligned Recurrent Network for Causal Video Object Detection,
ICIP19(3900-3904)
IEEE DOI
1910
Video object detection, attention mechanism, RNN, feature alignment
BibRef
Akal, O.,
Barbu, A.,
Learning Chan-Vese,
ICIP19(1590-1594)
IEEE DOI
1910
level sets, Chan-Vese segmentation,
convolutional neural networks, recurrent neural networks
BibRef
Le, H.D.,
Van Luong, H.,
Deligiannis, N.,
Designing Recurrent Neural Networks by Unfolding an L1-L1
Minimization Algorithm,
ICIP19(2329-2333)
IEEE DOI
1910
Sparse signal recovery, deep unfolding,
recurrent neural networks, l1-l1 minimization
BibRef
Hao, S.,
Miao, Z.,
Wang, J.,
Xu, W.,
Zhang, Q.,
Labanotation Generation Based on Bidirectional Gated Recurrent Units
with Joint and Line Features,
ICIP19(4265-4269)
IEEE DOI
1910
Labanotation, Motion capture data, Bi-GRU, Joint feature, Line feature
BibRef
Becker, S.[Stefan],
Hug, R.[Ronny],
Hübner, W.[Wolfgang],
Arens, M.[Michael],
An RNN-Based IMM Filter Surrogate,
SCIA19(387-398).
Springer DOI
1906
BibRef
Kimura, D.,
Chaudhury, S.,
Narita, M.,
Munawar, A.,
Tachibana, R.,
Adversarial Discriminative Attention for Robust Anomaly Detection,
WACV20(2161-2170)
IEEE DOI
2006
Anomaly detection, Training, Image reconstruction, Robustness,
Noise measurement, Generators, Visualization
BibRef
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,
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
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, 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
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.H.[Ju-Ho],
Bottom-Up Attention Guidance for Recurrent Image Recognition,
ICIP18(3004-3008)
IEEE DOI
1809
Computational modeling, Task analysis,
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
Li, M.M.[Ming-Ming],
Ge, S.S.[Shuzhi Sam],
Lee, T.H.[Tong Heng],
Glance and Glimpse Network:
A Stochastic Attention Model Driven by Class Saliency,
DeepVisual16(III: 572-587).
Springer DOI
1704
Attention-based recurrent neural network (Glimpse Network) and a
convolutional neural network (Glance Network).
BibRef
Vallet, A.,
Sakamoto, H.,
Convolutional Recurrent Neural Networks for Better Image
Understanding,
DICTA16(1-7)
IEEE DOI
1701
Convolution
BibRef
Sigurdsson, G.A.[Gunnar A.],
Chen, X.L.[Xin-Lei],
Gupta, A.[Abhinav],
Learning Visual Storylines with Skipping Recurrent Neural Networks,
ECCV16(V: 71-88).
Springer DOI
1611
BibRef
And: A2, A3, Only:
Webly Supervised Learning of Convolutional Networks,
ICCV15(1431-1439)
IEEE DOI
1602
Data models
BibRef
Shankar, T.,
Dwivedy, S.K.,
Guha, P.,
Reinforcement Learning via Recurrent Convolutional Neural Networks,
ICPR16(2592-2597)
IEEE DOI
1705
Belief propagation, Convolution,
Learning (artificial intelligence), Mathematical model,
Neural networks, Planning, Robots
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
Liang, M.[Ming],
Hu, X.L.[Xiao-Lin],
Recurrent convolutional neural network for object recognition,
CVPR15(3367-3375)
IEEE DOI
1510
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
BibRef
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 .