14.5.10.4.2 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],
Investigating the prediction capabilities of the simple recurrent neural network on real temporal sequences,
PR(33), No. 12, December 2000, pp. 2075-2081.
Elsevier DOI 0008
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Gupta, L.[Lalit], McAvoy, M.[Mark], Phegley, J.[James],
Classification of temporal sequences via prediction using the simple recurrent neural network,
PR(33), No. 10, October 2000, pp. 1759-1770.
Elsevier DOI 0006
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Delgado, M., Pegalajar, M.C.,
A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference,
PR(38), No. 9, September 2005, 1444-1456.
Elsevier DOI 0506
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Wang, J.S.[Jeen-Shing], Hsu, Y.L.[Yu-Liang], Lin, H.Y.[Hung-Yi], Chen, Y.P.[Yen-Ping],
Minimal model dimension/order determination algorithms for recurrent neural networks,
PRL(30), No. 9, 1 July 2009, pp. 812-819.
Elsevier DOI 0905
Model dimension/order determination; Nonlinear system identification; Recurrent neural networks; Minimal realization BibRef

Chatzis, S.P.[Sotirios P.], Demiris, Y.F.[Yi-Fannis],
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

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
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 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

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 Networks,
IEICE(E103-D), No. 12, December 2020, pp. 2457-2462.
WWW Link. 2012
BibRef

Zhu, L.[Li], Xie, Z.[Zihao], 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


An, J.B.[Joung-Bin], Kang, H.[Hyolim], Han, S.H.[Su Ho], Yang, M.H.[Ming-Hsuan], Kim, S.J.[Seon Joo],
MiniROAD: Minimal RNN Framework for Online Action Detection,
ICCV23(10307-10316)
IEEE DOI Code:
WWW Link. 2401
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 .


Last update:Mar 16, 2024 at 20:36:19