DTR-HAR: deep temporal residual representation for human activity recognition

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works

Speeded-Up Robust Features (SURF)

Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · see citing works

Learning Spatiotemporal Features with 3D Convolutional Networks

Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593 · see citing works

Two-Stream Convolutional Networks for Action Recognition in Videos

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works

Large-Scale Video Classification with Convolutional Neural Networks

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354 · cited 3× · see citing works

3D Convolutional Neural Networks for Human Action Recognition

Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,222 · see citing works

Long-term recurrent convolutional networks for visual recognition and description

Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works

On the difficulty of training recurrent neural networks

Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310 · see citing works

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Gool, L. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_2 · Cited by 4,164 · see citing works

Recurrent Neural Network RegularizationPreprint

Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv.org · doi:10.48550/arxiv.1409.2329 · Cited by 3,086 · see citing works

2009 International Joint Conference on Artificial Intelligence

(2009) · doi:10.1109/jcai15434.2009 · Cited by 2,846 · see citing works

Beyond short snippets: Deep networks for video classification

Ng, J. Y.-H., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., & Toderici, G. D. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299101 · Cited by 2,121 · see citing works

Unsupervised Learning of Video Representations using LSTMs

Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861 · see citing works

A Spatio-Temporal Descriptor Based on 3D-Gradients

Klaeser, A., Marszałek, M., & Schmid, C. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.99 · Cited by 1,842 · see citing works

Actions as space-time shapes

Blank, M., Gorelick, L., Shechtman, E., Irani, M., & Basri, R. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.28 · Cited by 1,760 · see citing works

A 3-dimensional sift descriptor and its application to action recognition

Scovanner, P., Ali, S., & Shah, M. A. (2007) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1291233.1291311 · Cited by 1,605 · see citing works

Mining actionlet ensemble for action recognition with depth cameras

Wang, J., Liu, Z., Wu, Y. N., & Yuan, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247813 · Cited by 1,556 · see citing works

HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences

Oreifej, O., & Liu, Z. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.98 · Cited by 950 · see citing works

An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector

Willems, G., Tuytelaars, T., & Gool, L. V. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_48 · Cited by 877 · see citing works

Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features

Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., & Baik, S. W. (2017) · IEEE Access · doi:10.1109/access.2017.2778011 · Cited by 751 · see citing works

Learning human activities and object affordances from RGB-D videos

Koppula, H. S., Gupta, R., & Saxena, A. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913478446 · Cited by 680 · see citing works

Video Salient Object Detection via Fully Convolutional Networks

Wang, W., Shen, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2754941 · Cited by 635 · see citing works

Learning Human-Object Interactions by Graph Parsing Neural Networks

Qi, S., Wang, W., Jia, B., Shen, J., & Zhu, S. C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_25 · Cited by 597 · see citing works

Unstructured human activity detection from RGBD images

Sung, J., Ponce, C., Selman, B., & Saxena, A. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224591 · Cited by 583 · see citing works

Differential Recurrent Neural Networks for Action Recognition

Veeriah, V., Zhuang, N., & Qi, G.-J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.460 · Cited by 525 · see citing works

Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification

Wu, Z., Wang, X., Jiang, Y., Ye, H., & Xue, X. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806222 · Cited by 483 · see citing works

A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

Ding, L., Fang, W., Luo, H., Love, P. E. D., Zhong, B., & Ouyang, X. (2017) · Automation in Construction · doi:10.1016/j.autcon.2017.11.002 · Cited by 479 · see citing works

Actions Sketch: A Novel Action Representation

Yılmaz, A., & Shah, M. A. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.58 · Cited by 424 · see citing works

Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition

Núñez, J. C., Cabido, R., Pantrigo, J. J., Montemayor, A. S., & Vélez, J. F. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.033 · Cited by 419 · see citing works

Jointly Learning Heterogeneous Features for RGB-D Activity Recognition

Hu, J., Zheng, W., Lai, J., & Zhang, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2640292 · Cited by 392 · see citing works

Jointly learning heterogeneous features for RGB-D activity recognition

Hu, J., Zheng, W., Lai, J., & Zhang, J. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299172 · Cited by 358 · see citing works

A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping

Wang, W., Shen, J., & Ling, H. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840724 · Cited by 350 · see citing works

Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning

Li, X., Shen, H., Zhang, L., Zhang, H., Yuan, Q., & Yang, G. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2307354 · Cited by 298 · see citing works

Learning discriminative representations from RGB-D video data

Liu, L., & Shao, L. (2013) · Cited by 265 · see citing works

TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition

Ma, C., Chen, M.-H., Kira, Z., & AlRegib, G. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.09.003 · Cited by 247 · see citing works

Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

Zhang, M., & Sawchuk, A. A. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2253613 · Cited by 188 · see citing works

Quadruplet Network With One-Shot Learning for Fast Visual Object Tracking

Dong, X., Shen, J., Wu, D., Guo, K., Jin, X., & Porikli, F. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2898567 · Cited by 176 · see citing works

Video Saliency Prediction Using Spatiotemporal Residual Attentive Networks

Lai, Q., Wang, W., Sun, H., & Shen, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2936112 · Cited by 158 · see citing works

Action detection in complex scenes with spatial and temporal ambiguities

Hu, Y., Cao, L., Lv, F., et al. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459153 · Cited by 150 · see citing works

Combining CNN streams of RGB-D and skeletal data for human activity recognition

Khaire, P., Kumar, P., & Imran, J. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.04.035 · Cited by 146 · see citing works

Cascaded Human-Object Interaction Recognition

Zhou, T., Wang, W., Qi, S., Ling, H., & Shen, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00432 · Cited by 136 · see citing works

Cognitive Analysis of Working Memory Load from Eeg, by a Deep Recurrent Neural Network

Kuanar, S. P., Athitsos, V., Pradhan, N., Mishra, A., & Rao, K. R. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462243 · Cited by 132 · see citing works

Local Semantic Siamese Networks for Fast Tracking

Liang, Z., & Shen, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2959256 · Cited by 130 · see citing works

Hierarchical Human Parsing With Typed Part-Relation Reasoning

Wang, W., Zhu, H., Dai, J., Pang, Y., Shen, J., & Shao, L. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00895 · Cited by 123 · see citing works

Using unlabeled data in a sparse-coding framework for human activity recognition

Bhattacharya, S., Nurmi, P., Hammerla, N., & Plötz, T. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.05.006 · Cited by 116 · see citing works

Two-stream RNN/CNN for action recognition in 3D videos

Zhao, R., Ali, H., & van der Smagt, P. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206288 · Cited by 116 · see citing works

Evaluating spatiotemporal interest point features for depth-based action recognition

Zhu, Y. M., Chen, W., & Guo, G. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.04.005 · Cited by 114 · see citing works

Multilevel Depth and Image Fusion for Human Activity Detection

Ni, B., Pei, Y., Moulin, P., & Yan, S. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2276433 · Cited by 111 · see citing works

deepGesture: Deep learning-based gesture recognition scheme using motion sensors

Kim, J., Hong, G.-S., Kim, B., & Dogra, D. P. (2018) · Displays · doi:10.1016/j.displa.2018.08.001 · Cited by 103 · see citing works

Bilinear heterogeneous information machine for RGB-D action recognition

Kong, Y., & Fu, Y. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298708 · Cited by 82 · see citing works

Interaction part mining: A mid-level approach for fine-grained action recognition

Zhou, Y., Ni, B., Hong, R., Wang, M., & Tian, Q. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298953 · Cited by 78 · see citing works

Improved human action recognition approach based on two-stream convolutional neural network model

Liu, C., Ying, J., Yang, H., Hu, X., & Liu, J. (2020) · The Visual Computer · doi:10.1007/s00371-020-01868-8 · Cited by 65 · see citing works

Spatio-temporal feature extraction and representation for RGB-D human action recognition

Luo, J., Wang, W., & Qi, H. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.03.024 · Cited by 62 · see citing works

Low Dose Abdominal CT Image Reconstruction: An Unsupervised Learning Based Approach

Kuanar, S. P., Athitsos, V., Mahapatra, D., Rao, K. R., Akhtar, Z., & Dasgupta, D. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803037 · Cited by 56 · see citing works

3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition

Arif, S., Wang, J., Hassan, T. U., & Fei, Z. (2019) · Future Internet · doi:10.3390/fi11020042 · Cited by 55 · see citing works

Self-Learning With Rectification Strategy for Human Parsing

Li, T., Liang, Z., Zhao, S., Gong, J., & Shen, J. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00928 · Cited by 37 · see citing works

Human Activity Recognition with Pose-driven Attention to RGB

Baradel, F., Wolf, C., & Mille, J. (2018) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 36 · see citing works

Deep-Temporal LSTM for Daily Living Action Recognition

Das, S., Koperski, M., Brémond, F., & Francesca, G. (2018) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639122 · Cited by 26 · see citing works

A four-stream ConvNet based on spatial and depth flow for human action classification using RGB-D data

Srihari, D., Kishore, P. V. V., Kumar, E. K., Kumar, D. A., Kumar, M. T. K., Prasad, M. V. D., & Prasad, C. R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08588-9 · Cited by 26 · see citing works

Two deep approaches for ADL recognition: A multi-scale LSTM and a CNN-LSTM with a 3D matrix skeleton representation

Ercolano, G., Riccio, D., & Rossi, S. (2017) · IEEE RO-MAN · doi:10.1109/roman.2017.8172406 · Cited by 25 · see citing works

Real-time multimodal ADL recognition using convolution neural networks

Madhuranga, D., Madushan, R., Siriwardane, C., & Gunasekera, K. (2020) · The Visual Computer · doi:10.1007/s00371-020-01864-y · Cited by 23 · see citing works

Action recognition based on a mixture of RGB and depth based skeleton

Das, S., Koperski, M., Brémond, F., & Francesca, G. (2017) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2017.8078548 · Cited by 22 · see citing works

Modeling spatial layout of features for real world scenario RGB-D action recognition

Koperski, M., & Brémond, F. (2016) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2016.7738023 · Cited by 14 · see citing works

Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos

Asadi, M., & Kasaei, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5017-y · Cited by 13 · see citing works

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

Accurate And Fast Fine-Grained Image Classification via Discriminative Learning

Wang, Z., Wang, S., Zhang, P., Li, H., & Liu, B. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00115 · Cited by 3 · see citing works

Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative FeedbackPreprint

Prabhudesai, M., Ke, T.-W., Li, A. C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.16102

Stable Discriminative Dictionary Learning Via Discriminative Deviation

Khan, N., & Tappen, M. F. (2012) · Journal of International Crisis and Risk Communication Research · Cited by 6 · see citing works

Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences

Moghimi, M. K., Kerr, J., Johnson, E., Godbole, S. V., & Belongie, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14442-9_42 · Cited by 2 · see citing works

Searching and Learning Discriminative Regions for Fine-Grained Image Retrieval and Classification

Kangbo, S. U. N., & Zhu, J. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7094 · Cited by 6 · see citing works