Temporal Sequence Modeling for Video Event Detection
Abstract
We present a novel approach for event detection in video by temporal sequence modeling. Exploiting temporal information has lain at the core of many approaches for video analysis (i.e., action, activity and event recognition). Unlike previous works doing temporal modeling at semantic event level, we propose to model temporal dependencies in the data at sub-event level without using event annotations. This frees our model from ground truth and addresses several limitations in previous work on temporal modeling. Based on this idea, we represent a video by a sequence of visual words learnt from the video, and apply the Sequence Memoizer [21] to capture long-range dependencies in a temporal context in the visual sequence. This data-driven temporal model is further integrated with event classification for jointly performing segmentation and classification of events in a video. We demonstrate the efficacy of our approach on two challenging datasets for visual recognition.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The proposed model achieves state-of-the-art results on 50Salads, GTEA, and the Breakfast dataset.
“Our model achieves state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.”
From MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation (2019) · cites this paper as [12]
Cites
The Hungarian method for the assignment problem
Kuhn, H. W. (1955) · Naval Research Logistics Quarterly · doi:10.1002/nav.3800020109 · Cited by 13,120 · see citing works
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · 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.2008.4587756 · Cited by 3,481 · see citing works
(1992) · doi:10.5555/176313.176316 · Cited by 2,892 · see citing works
On the algorithmic implementation of multiclass kernel-based vector machines
Crammer, K., & Singer, Y. (2002) · Cited by 1,902 · see citing works
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
Niebles, J. C., Wang, H., & Fei-Fei, L. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0122-4 · Cited by 1,475 · see citing works
On the algorithmic implementation of multiclass kernel-based vector machines
CrammerKoby, & SingerYoram (2002) · Journal of Machine Learning Research · doi:10.5555/944790.944813 · Cited by 876 · see citing works
Ryoo, M. S., & Aggarwal, J. K. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459361 · Cited by 582 · see citing works
Efficient visual event detection using volumetric features
Ke, Y., Sukthankar, R., & Hebert, M. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.85 · Cited by 534 · see citing works
Selected Studies of the Principle of Relative Frequency in Language
Zipf, G. (2014) · Harvard University Press eBooks · doi:10.4159/harvard.9780674434929 · Cited by 461 · see citing works
Learning latent temporal structure for complex event detection
Tang, K., Fei-Fei, L., & Koller, D. (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.2012.6247808 · Cited by 359 · see citing works
Human Activity Recognition with Metric Learning
Tran, D., & Sorokin, A. A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_42 · Cited by 335 · see citing works
MoSIFT: Recognizing Human Actions in Surveillance Videos
Chen, M.-Y., & Hauptmann, A. (2018) · KiltHub Repository · doi:10.1184/r1/6607523 · Cited by 309 · see citing works
A Markov Clustering Topic Model for mining behaviour in video
Hospedales, T. M., Gong, S., & Xiang, T. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459342 · Cited by 244 · see citing works
Joint segmentation and classification of human actions in video
Hoai, M., Lan, Z., & la Torre, F. D. (2011) · doi:10.1109/cvpr.2011.5995470 · Cited by 238 · see citing works
\nTrecvid 2012 - an overview of the goals, tasks, data, evaluation mechanisms and metrics
Over, P. L., Awad, G., Michel, M., Fiscus, J., Sanders, G., Shaw, B., Kraaij, W., Smeaton, A. F., & Quéot, G. (2013) · Radboud Repository (Radboud University) · Cited by 177 · see citing works
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
Oh, S. M., Rehg, J. M., Balch, T. R., & Dellaert, F. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0062-z · Cited by 151 · see citing works
Spatio-Temporal Phrases for Activity Recognition
Zhang, Y., Liu, X., Chang, M., Ge, W., & Chen, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33712-3_51 · Cited by 130 · see citing works
Leveraging temporal, contextual and ordering constraints for recognizing complex activities in video
Laxton, B., Lim, J., & Kriegman, D. J. (2007) · 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.2007.383074 · Cited by 121 · see citing works
Large-scale event detection using semi-hidden Markov models
Hongeng, S., & Nevatia, R. (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238661 · Cited by 109 · see citing works
Discriminative human action segmentation and recognition using semi-Markov model
Shi, Q., Wang, L., Cheng, L., & Smola, A. J. (2008) · 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.2008.4587557 · Cited by 94 · see citing works
A stochastic memoizer for sequence data
Wood, F., Archambeau, C., Gasthaus, J. A., James, L. F., & Teh, Y. W. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553518 · Cited by 92 · see citing works
Wood, F., Gasthaus, J. A., Archambeau, C., James, L. F., & Teh, Y. W. (2011) · Communications of the ACM · doi:10.1145/1897816.1897842 · Cited by 51 · see citing works
Recognition of repetitive sequential human activity
Fan, Q., Bobbitt, R., Zhai, Y., Yanagawa, A., Pankanti, S., & Hampapur, A. (2009) · 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.2009.5206644 · Cited by 34 · see citing works
Self-citationSpatio-temporal fisher vector coding for surveillance event detection
Chen, Q. K., Cai, Y., Brown, L., Datta, A., Fan, Q., Feris, R. S., Yan, S., Hauptmann, A. G., & Pankanti, S. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502155 · Cited by 15 · see citing works
Self-citation
Flagged first, then by citations
4 of the 28 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A Survey of Model Compression and Acceleration for Deep Neural Networks
Cheng, Y., Wang, D., Zhou, P., & Zhang, T. (2017) · arXiv · doi:10.48550/arxiv.1710.09282 · Cited by 857 · see citing works
Self-citationMS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Farha, Y. A., & Gall, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00369 · Cited by 673 · see citing works
“[4] represent videos as a sequence of visual words, and model the temporal dependency by employing a Bayesian non-parametric model of discrete sequences to jointly classify and segment video sequences.”
MethodJointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification
Xu, S., Cheng, Y., Gu, K., Yang, Y., Chang, S., & Zhou, P. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.507 · Cited by 351 · see citing works
Self-citationMS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3021756 · Cited by 349 · see citing works
“[21] represent videos as a sequence of visual words, and model the temporal dependency by employing a Bayesian non-parametric model of discrete sequences to jointly classify and segment video sequences.”
MethodTemporal Action Detection Using a Statistical Language Model
Richard, A., & Gall, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.341 · Cited by 215 · see citing works
An end-to-end generative framework for video segmentation and recognition
Kuehne, H., Gall, J., & Serre, T. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477701 · Cited by 188 · see citing works
Improving Action Segmentation via Graph-Based Temporal Reasoning
Huang, Y., Sugano, Y., & Sato, Y. (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/cvpr42600.2020.01404 · Cited by 160 · see citing works
Context-aware Biaffine Localizing Network for Temporal Sentence Grounding
Liu, D., Qu, X., Dong, J., Zhou, P., Cheng, Y., Wei, W., Xu, Z., & Xie, Y. (2021) · 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/cvpr46437.2021.01108 · Cited by 141 · see citing works
Self-citationUnsupervised Sequential Outlier Detection With Deep Architectures
Lu, W., Cheng, Y., Xiao, C., Chang, S., Huang, S., Liang, B., & Huang, T. S. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713048 · Cited by 121 · see citing works
Self-citationWeakly supervised learning of actions from transcripts
Kuehne, H., Richard, A., & Gall, J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.06.004 · Cited by 119 · see citing works
Temporal Action Segmentation: An Analysis of Modern Techniques
Ding, G., Şener, F., & Yao, A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3327284 · Cited by 124 · see citing works
Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data
Wang, J., Cheng, Y., & Feris, R. (2016) · 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.2016.252 · Cited by 97 · see citing works
Self-citationModeling Temporal Structure with LSTM for Online Action Detection
Geest, R. D., & Tuytelaars, T. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00173 · Cited by 92 · see citing works
Fine-grained Iterative Attention Network for Temporal Language Localization in Videos
Qu, X., Tang, P., Zou, Z., Cheng, Y., Dong, J., Zhou, P., & Xu, Z. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414053 · Cited by 92 · see citing works
Self-citationGlobal2Local: Efficient Structure Search for Video Action Segmentation
Gao, S., Han, Q., Li, Z., Peng, P., Wang, L., & Cheng, M. (2021) · 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/cvpr46437.2021.01653 · Cited by 74 · see citing works
Ma, Z., Chang, X., Yang, Y., Sebe, N., & Hauptmann, A. G. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2659221 · Cited by 65 · see citing works
Violin: A Large-Scale Dataset for Video-and-Language Inference
Liu, J., Chen, W., Cheng, Y., Gan, Z., Yu, L., Yang, Y., & Liu, 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/cvpr42600.2020.01091 · Cited by 62 · see citing works
Self-citationRF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Gao, S., Li, Z., Qi, H., Cheng, M., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183829 · Cited by 57 · see citing works
Temporal Relational Modeling with Self-Supervision for Action Segmentation
Wang, D., Hu, D., Li, X., & Dou, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i4.16377 · Cited by 54 · see citing works
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49 · see citing works
Complex Event Detection by Identifying Reliable Shots from Untrimmed Videos
Fan, H., Chang, X., Cheng, D., Yang, Y., Xu, D., & Hauptmann, A. G. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.86 · Cited by 49 · see citing works
Dynamic Concept Composition for Zero-Example Event Detection
Chang, X., Yang, Y., Long, G., Zhang, C., & Hauptmann, A. G. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10474 · Cited by 42 · see citing works
Learning Composite Latent Structures for 3D Human Action Representation and Recognition
Wei, P., Sun, H., & Zheng, N. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2897902 · Cited by 42 · see citing works
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers
Chang, X., Yu, Y., Yang, Y., & Xing, E. P. (2016) · 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.2016.208 · Cited by 31 · see citing works
Fusion strategies for recognition of violence actions
Lejmi, W., Khalifa, A. B., & Mahjoub, M. A. (2017) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2017.193 · Cited by 25 · see citing works
Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference
Li, J., Tang, S., Zhu, L., Shi, H., Huang, X., Wu, F. Q., Yang, Y., & Zhuang, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00188 · Cited by 24 · see citing works
Video Moment Retrieval with Hierarchical Contrastive Learning
Zhang, B., Yang, C., Jiang, B., & Zhou, X. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547963 · Cited by 22 · see citing works
Event detection in video sequences: Challenges and perspectives
Lejmi, W., Mahjoub, M. A., & Khalifa, A. B. (2017) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/fskd.2017.8393354 · Cited by 21 · see citing works
Scalable Video Event Retrieval by Visual State Binary Embedding
Yu, L., Huang, Z. H., Cao, J., & Shen, H. T. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557059 · Cited by 19 · see citing works
Gated forward refinement network for action segmentation
Wang, D., Yuan, Y., & Wang, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.066 · Cited by 18 · see citing works
TSRN: two-stage refinement network for temporal action segmentation
Tian, X., Jin, Y., & Tang, X. (2023) · Pattern Analysis and Applications · doi:10.1007/s10044-023-01166-8 · Cited by 18 · see citing works
Real-time gymnast detection and performance analysis with a portable 3D camera
Reily, B., Zhang, H., & Hoff, W. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.11.006 · Cited by 17 · see citing works
Event Detection in Continuous Video: An Inference in Point Process Approach
Qin, Z., & Shelton, C. R. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2745209 · Cited by 15 · see citing works
Analyzing periodicity and saliency for adult video detection
Liu, Y., Gu, X., Huang, L., Ouyang, J., Liao, M., & Wu, L. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7576-6 · Cited by 15 · see citing works
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Li, S., Farha, Y. A., Liu, Y., Cheng, M.-M., & Gall, J. (2020) · arXiv · Cited by 350 · see citing works
“[21] represent videos as a sequence of visual words, and model the temporal dependency by employing a Bayesian non-parametric model of discrete sequences to jointly classify and segment video sequences.”
MethodMT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
Ding, H., Lou, S., Ye, H., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app15126773 · Cited by 14 · see citing works
Sequence Classification Restricted Boltzmann Machines With Gated Units
Tran, S. N., Garcez, A. D., Weyde, T., Yin, J., Zhang, Q., & Karunanithi, M. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2958103 · Cited by 13 · see citing works
Dilated Transformer with Feature Aggregation Module for Action Segmentation
Du, Z., & Wang, Q. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11133-9 · Cited by 13 · see citing works
Kim, Y., Chen, J., Chang, M., Wang, X., Provost, E. M., & Lyu, S. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163130 · Cited by 12 · see citing works
Towards Neuro-Symbolic Video Understanding
Choi, M., Goel, H., Omama, M., Yang, Y., Shah, S., & Chinchali, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73229-4_13 · Cited by 11 · see citing works
Social Role Identification via Dual Uncertainty Minimization Regularization
Cheng, Y., Agrawal, A., Choudhary, A., Liu, H., & Zhang, T. (2014) · Proceedings · doi:10.1109/icdm.2014.31 · Cited by 10 · see citing works
Self-citationZero-Shot Video Event Detection With High-Order Semantic Concept Discovery and Matching
Jin, Y., Jiang, W., Yang, Y., & Mu, Y. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3073624 · Cited by 10 · see citing works
Goldbraikh, A., Shubi, O., Rubin, O., Pugh, C. M., & Laufer, S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110778 · Cited by 9 · see citing works
Machine learning for video event recognition
Avola, D., Cascio, M., Cinque, L., Foresti, G. L., & Pannone, D. (2021) · Integrated Computer-Aided Engineering · doi:10.3233/ica-210652 · Cited by 9 · see citing works
Liu, Y., Zhong, X., Zhai, S., Du, Z., Gao, Z., Huang, Q., Zhang, C. Y., Jiang, B., Pandey, V. K., Han, S., et al. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107672 · Cited by 9 · see citing works
Automated Quantization and Retraining for Neural Network Models Without Labeled Data
Thonglek, K., Takahashi, K., Ichikawa, K., Nakasan, C., Nakada, H., Takano, R., Leelaprute, P., & Iida, H. (2022) · IEEE Access · doi:10.1109/access.2022.3190627 · Cited by 8 · see citing works
Carvajal, J., McCool, C., Lovell, B. C., & Sanderson, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42996-0_10 · Cited by 8 · see citing works
The forgettable-watcher model for video question answering
Chu, W., Xue, H., Zhao, Z., Cai, D., & Yao, C. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.069 · Cited by 8 · see citing works
RiskWheel: Interactive visual analytics for surveillance event detection
Cheng, Y., Brown, L., Fan, Q., Feris, R. S., Pankanti, S., & Zhang, T. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890286 · Cited by 7 · see citing works
Self-citationAffective interaction recognition using spatio-temporal features and context
Liang, J., Xu, C., Feng, Z., & Ma, X. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.10.008 · Cited by 7 · see citing works
Gao, Y., Lin, Q., Ye, S., Cheng, Y., Zhang, T., Liang, B., & Lu, W. (2024) · Displays · doi:10.1016/j.displa.2024.102775 · Cited by 6 · see citing works
Self-citationAction recognition by Latent Duration Model
Wang, T., Liu, C., & Wang, L.-T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.057 · Cited by 6 · see citing works
Manjula, S., & Lakshmi, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2919-8_5 · Cited by 5 · see citing works
Temporal Human Action Segmentation via Dynamic ClusteringPreprint
Zhang, Y., Sun, H., Tang, S.-Y., & Neumann, H. (2018) · arXiv.org · doi:10.48550/arxiv.1803.05790 · Cited by 6 · see citing works
Human activities prediction by learning combinatorial sparse representations
Xu, K., Qin, Z., & Wang, G. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532452 · Cited by 4 · see citing works
Kim, Y. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344653 · Cited by 4 · see citing works
A discriminative structural model for joint segmentation and recognition of human actions
Liu, C., Hou, J., Wu, X., & Jia, Y. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6189-9 · Cited by 4 · see citing works
Weakly supervised learning of actions from transcriptsPreprint
Kuehne, H., Richard, A., & Gall, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02237 · Cited by 4 · see citing works
Throughout Procedural Transformer for Online Action Detection and Anticipation
Yuan, H., Chen, Y., Ji, Z., Zheng, Z., Gu, Y., & Zhou, J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3559991 · Cited by 2 · see citing works
Improving action segmentation via explicit similarity measurement
Kamel, A., Zhang, W., Li, A., & Zhang, C. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113251 · Cited by 2 · see citing works
Physics-informed GCN-LSTM framework for long-term forecasting of 2D and 3D microstructure evolution
Razavi, H., & Nele Moelans, B. (2026) · npj Computational Materials · doi:10.1038/s41524-026-01999-x · Cited by 2 · see citing works
Long-Tail Temporal Action Segmentation with Group-Wise Temporal Logit Adjustment
Pang, Z., Şener, F., Ramasubramanian, S., & Yao, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73404-5_19 · Cited by 2 · see citing works
Legislative prediction with dual uncertainty minimization from heterogeneous information
Cheng, Y., Agrawal, A., Liu, H., & Choudhary, A. (2016) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11309 · Cited by 2 · see citing works
Self-citationDual Uncertainty Minimization Regularization and Its Applications on Heterogeneous Data
Cheng, Y., Choudhary, A., Wang, J., Pankanti, S., & Liu, H. (2014) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2014.138 · Cited by 2 · see citing works
Self-citationTowards a generative approach to activity recognition and segmentation.Preprint
Kuehne, H., & Serre, T. (2015) · arXiv (Cornell University) · Cited by 2 · see citing works
A Novel Unsupervised Method for Temporal Segmentation of Videos
Shi, X., Lu, Y., Liu, C., Zhang, D., & Liu, F. (2017) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv.2017.00017 · Cited by 1 · see citing works
Ding, M., Ding, Y., Zhang, Z., Liu, R., & Tang, B. (2025) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2025.3622577 · Cited by 1 · see citing works
Temporal Pattern Localization using Mixed Integer Linear Programming
Zhao, R., Schalk, G., & Ji, Q. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8546083 · Cited by 1 · see citing works
IBM-Northwestern@Trecvid 2014: Surveillance event detection
Cheng, Y., Brown, L. M., Fan, Q., Liu, J., Feris, R., Choudhary, A., & Pankanti, S. (2014) · Cited by 1 · see citing works
Self-citationCooking in the kitchen: Recognizing and Segmenting Human Activities in VideosPreprint
Kuehne, H., Gall, J., & Serre, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.06073 · Cited by 1 · see citing works
Kuehne, H., & Serre, T. (2015) · arXiv (Cornell University)
Action Graph Decomposition Based on Sparse Coding
Feng, W., Tian, H., Xiao, Y., Ding, J., & Tang, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71607-7_5
A Novel Double-Layer Framework for Joint Segmentation and Recognition of Multiple Actions
Liu, C. (2018) · International Journal of Performability Engineering · doi:10.23940/ijpe.18.01.p11.101110
Semantic Sequence Analysis for Human Activity Prediction
Wang, G., Qin, Z., & Xu, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-77380-3_26
Unsupervised Activity Modelling in a Video
Agrawal, A. K. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3067-5_21
Notice of Retraction: Cascade Network with Boundary-Adaptive Filter for Surgical Gesture Recognition
Chen, H., Ni, Z.-L., Li, Z., & Bian, G. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533664
Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection
Wang, L., Huang, C., & Chen, X. (2023) · International Conference on Automation, Control and Robotics Engineering (CACRE) · doi:10.1109/cacre58689.2023.10208414
SmallTAL: Real-Time Egocentric Online Temporal Action Localization for the Data-Impoverished
Joyce, E. C., Chen, Y., Neeter, E., & Mordohai, P. (2023) · PRESENCE Virtual and Augmented Reality · doi:10.1162/pres_a_00408
GCN Rewarded Reinforcement Learning for Action Segmentation
Wang, L., Liu, J., Hu, X., & Huo, G. (2024) · International Joint Conference on Robotics and Artificial Intelligence (JCRAI) · doi:10.1145/3696474.3696475
- Authors
- Cheng, Y., Fan, Q., Pankanti, S., & Choudhary, A.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Northwestern University (US)
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 2235-2242
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2083319857
- Cited by
- 80
- References
- 28
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 4 · 2021: 9 · 2020: 6 · 2019: 3 · 2018: 7 · 2017: 13 · 2016: 13 · 2015: 6 · 2014: 4
- FWCI
- 6.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- video event detection, temporal sequence modeling, visual words, long-range temporal dependencies, event segmentation, event classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
