Multiview Transformers for Video 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

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

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

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

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

AST-Adapter: Parameter-Efficient Video-to-Video Transfer Learning With Adaptive Spatiotemporal Information Bias

Hou, P., Li, G., Cai, Z., Zhang, J., & Huang, D. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3638017 · Cited by 2

Ensemble Encoder-Enabled Proactive Human Assembly Intention Recognition With Multimodal and Flexible Scale Data

Ma, D., Zhang, C., Zhou, G., & Ma, C. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3650942 · Cited by 2

Snakes and Ladders: Two Steps Up for VideoMamba

Lü, H., Salah, A. A., & Poppe, R. W. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02246 · Cited by 2

Learning Unified Spatio-temporal Representations for Efficient Compressed Video Understanding

Biswas, S. D., Soufleri, E., Roy, A., & Roy, K. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00462 · Cited by 2

GSoANet: Group Second-Order Aggregation Network for Video Action Recognition

Wang, Z., Dong, W., Zhang, B., Zhang, J., Liu, X., Liu, B., & Zhang, Q. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11270-9 · Cited by 2

Spatio-Temporal Crop Aggregation for Video Representation Learning

Sameni, S., Jenni, S., & Favaro, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00521 · Cited by 2

Multi-level Temporal Information Sharing Transformer-Based Feature Reuse Network for Cardiac MRI Reconstruction

Wang, G., Lyu, J., Wang, F., Wang, C., & Qin, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-52448-6_39 · Cited by 2

TEAM: Transformer Encoder Attention Module for Video Classification

Park, H. S., & Choi, Y. S. (2024) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.043245 · Cited by 2

Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

Wang, Z., & Yao, L. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048304 · Cited by 2

Gesture Recognition on Video Data

Gunkin, G., & Makarov, I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-67008-4_14 · Cited by 3

Spatial-Temporal Transformer Network for Continuous Action Recognition in Industrial Assembly

Huang, J., Liu, X., Hu, H., Tang, S., Li, C., Zhao, S., Lin, Y., Wang, K., Liu, Z., & Lian, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5609-4_9 · Cited by 2

Poster: Multimodal ConvTransformer for Human Activity Recognition

Haque, S. T., & Ngu, A. H. H. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00041 · Cited by 2

A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives

Peirone, S., Pistilli, F., Alliegro, A., & Averta, G. (2024) · 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/cvpr52733.2024.01730 · Cited by 2

BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition

He, H., Liu, W., & Xing, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0908-6_15 · Cited by 2

Enhancing skeleton-based action recognition with hybrid real and gan-generated datasets

Islamgozhayev, T., Amirgaliyev, B., & Kozhirbayev, Z. (2024) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2024.317092 · Cited by 2

The TSformer: A Non-Autoregressive Spatio-Temporal Transformers for 30-Day Ocean Eddy-Resolving Forecasting

Wang, G., Hou, M., Qin, M., Wu, X., Gao, Z., Chao, G., & Zhang, X. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13050966 · Cited by 5

Efficient High-Order Spatial Interactions for Visual Perception

Liu, Z., Rao, Y., Zhao, W., Zhou, J., & Lu, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3603181 · Cited by 2

MCV-Intention: A Multimodalities and Cross-View Dataset for Human Assembly Intention Recognition

Ma, D., Zhang, C., Xu, Q., & Zhou, G. (2025) · Scientific Data · doi:10.1038/s41597-025-06063-9 · Cited by 2

Adapting CLIP for Action Recognition via Dual Semantic Supervision and Temporal Prompt Reparameterization

Deng, L., Tan, J., & Liu, F. (2024) · Electronics · doi:10.3390/electronics13163348 · Cited by 1

Saliency-Guided Fine-Grained Temporal Mask Learning for Few-Shot Action Recognition

Zheng, S., Dang, Y., Chen, P., Huan, R., Zhao, D., & Liang, R. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681200 · Cited by 1

Learning Discriminative Representations in Videos via Active Embedding Distance Correlation

Zhao, Q., Wang, Y., He, Y., Qiao, Y., & Zhao, C. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3495564 · Cited by 1

Second-order transformer network for video recognition

Zhang, J., Dong, W., Wang, Z., Zhang, J., & Sun, Q. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.067 · Cited by 1

ACformer: A unified transformer for arbitrary-frame image exposure correction

Li, C., Yang, Y., He, Q., Gu, R., Lei Zhang, A., & Xu, J. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107162 · Cited by 1

An efficient video transformer network with token discard and keyframe enhancement for action recognition

Zhang, Q., Yang, Z., Shao, M., & Liang, H. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-06927-2 · Cited by 1

LTD-NET: Lightweight Two-Stream Decoder Net for Action Recognition

Ming, Y., Zhou, J., Chen, Z., & Hu, Y. (2024) · International Conference on Signal Processing · doi:10.1109/icsp62129.2024.10846570 · Cited by 1

LkSFocalNets: Video Action Recognition With Large Kernel Selective Focal Networks

Xiao, J., Ping, S., & Li, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888662 · Cited by 1

Improving classifier decision boundaries and interpretability using nearest neighbors

Schneider, J., & Casanova, A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00369-8 · Cited by 1

MCSTN: A Novel Multi-feature Fusion-Based Action Recognition Model

Wei, W., Li, A., & Duan, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9908-7_23 · Cited by 1

Dual Structure-guided Contrastive Network for Incomplete Multi-view Partial Multi-label Classification

Xu, K., Wu, Y., Wu, S., Miao, X., Chao, G., Zhu, M., Xi, M., & Zhao, X. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736916 · Cited by 1

Clip4Vis: Parameter-free fusion for multimodal video recognition

Zheng, Q., He, M., Duan, J., Luo, G., Wu, P., Han, Y., Min, Q., Peng, C., & Zhang, P. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131046 · Cited by 1

Three-Branch Network for Multi-Scale Spatiotemporal Feature Fusion in Remote Physiological Measurement

Li, Z., Xiao, H., Xia, Z., Zhou, F., Huang, X., & Liu, T. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3608082 · Cited by 1

Real-Time Ultra-Fine-Grained Surgical Instrument Classification

Atabuzzaman, M., DiMatteo, G., Al-Omari, H., Tang, C., Hale, C., Goode, A. E., King, D. R., & Thomas, C. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00195 · Cited by 1

DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition

Ullah, H., Shafique, M. A., Khan, A., & Munir, A. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3615909 · Cited by 1

Hier-EgoPack: Hierarchical Egocentric Video Understanding With Diverse Task Perspectives

Peirone, S. A., Pistilli, F., Alliegro, A., Tommasi, T., & Averta, G. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3621326 · Cited by 1

Progressive Learning of Instance-Level Proxy Semantics for Few-Shot Action Recognition

Peng, F., Yang, X., Wang, Y., & Xu, C. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3632652 · Cited by 1

A Transformer-based neural network for global short-range dust forecasting

Du, S., Chen, S., He, J., Fu, Y., & Lian, L. (2026) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2026.106898 · Cited by 2

GLFormer: a hierarchical cross-layer transformer decoding framework with global–local feature collaboration for video action recognition

Wu, H., Ma, X., Li, X., & Song, R. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02314-3 · Cited by 1

Distillation-free Scaling of Large State-Space Models for Images and Videos

Suleman, H., Wasim, S. T., Naseer, M., & Gall, J. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02824-0 · Cited by 5

Improving the generalization of ViTs for action understanding with VLM pre-training

Lü, H., Salah, A. A., & Poppe, R. W. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113794 · Cited by 1

ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning

Lee, J., Bae, K., Min, K., Park, G.-M., & Choi, J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01630 · Cited by 1

Diffusion-Based Action Recognition Generalizes to Untrained Domains

Guimarães, R., Xiao, F., Perona, P., & Marks, M. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00573 · Cited by 1

Learning Sequential Transformation Information of Ingredients for Fine-Grained Cooking Activity Recognition

Okamoto, A., Inoue, K., & Yoshioka, M. (2022) · doi:10.1145/3552485.3554940 · Cited by 1

A Novel Transformer-based Framework for Multi-View 3D Human Mesh Reconstruction

Chen, E., Ju, B., Jiang, L., & Zhao, D. (2022) · International Conference on Networking Systems of AI (INSAI) · doi:10.1109/insai56792.2022.00042 · Cited by 2

Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization

Yu, J., Zheng, Y., Ruan, S., Liu, Q., Cheng, Z., & Wu, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/186 · Cited by 1

Trajectory Alignment based Multi-Scaled Temporal Attention for Efficient Video Transformer

Zhang, Z., Yuan, D., Zhang, Y., & Bao, W. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00244 · Cited by 1

Leaping Into Memories: Space-Time Deep Feature Synthesis

Stergiou, A., & Deligiannis, N. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00188 · Cited by 1

A novel approach for enhanced abnormal action recognition via coarse and precise detection stage

Lei, Y., Ding, M., Lu, T., Li, J., Zhao, D., & Chen, F. (2024) · Electronic Research Archive · doi:10.3934/era.2024042 · Cited by 1

Spatiotemporal Representation Enhanced ViT for Video Recognition

Li, M., Li, F., Meng, B., Bai, R., Ren, J., Huang, Z., & Gao, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53305-1_3 · Cited by 1

Semantic-Driven Cross-Modal Obstacle Detection for Automatic Guided Vehicles in IIoT

Liu, H., Zhao, W., Li, S., & Gao, Y. (2023) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp58612.2023.10405134 · Cited by 1

RaSTFormer: region-aware spatiotemporal transformer for visual homogenization recognition in short videos

Zhang, S., Zhang, J., Zhang, H., & Li, Z. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09633-x · Cited by 1

Few-Shot Event Classification in Images using Knowledge Graphs for Prompting

Tahmasebzadeh, G., Springstein, M., Ewerth, R., & Müller-Budack, E. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00712 · Cited by 1

Multi-granularity transformer fusion for temporal action localization

Zhang, M., Hu, H., & Li, Z. (2024) · Soft Computing · doi:10.1007/s00500-024-09955-x · Cited by 1

Novel Design Ideas that Improve Video-Understanding Networks with Transformers

Hu, Y., & Barth, E. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10649969 · Cited by 1

Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion

Chen, A., & Liu, W. (2024) · Sensors · doi:10.3390/s24237536 · Cited by 1

STA+: Spatiotemporal Adaptation with Adaptive Model Selection for Video Action Recognition

Li, M., Zhang, C., & Zheng, X. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778906 · Cited by 1

The future of action recognition: are multi-modal visual language models the key?

Gümüşkaynak, E., & Eken, S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03951-w · Cited by 3

Modality mixer exploiting complementary information for multi-modal action recognition

Lee, S., Woo, S., Nugroho, M. A., & Kim, C. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104358 · Cited by 1

Multi-level Multi-modal Feature Fusion for Action Recognition in Videos

Hu, X., Ji, Y., & Gedamu, K. (2022) · International Workshop on Human-centric Multimedia Analysis (HCMA) · doi:10.1145/3552458.3556449 · Cited by 1

Relational Action Bank with Semantic–Visual Attention for Few-Shot Action Recognition

Liang, H., Du, J., Zhang, H., Han, B., & Ma, Y. (2023) · Future Internet · doi:10.3390/fi15030101

Real-time Steal Recognition on CCTV-Based Videos for Embedded Systems

Kerachi, S., Koma, A. K., & Asharioun, H. (2023) · International Computer Conference, Computer Society of Iran (CSICC) · doi:10.1109/csicc58665.2023.10105393

Frames Extraction from Table Tennis Competition Videos for Action Classification Using Optical Flow and Fuzzy Rules

Wang, C.-J., Chang, J.-R., Lin, H. Y., & Lu, C. J. (2023) · International Conference on Applied System Innovation · doi:10.1109/icasi57738.2023.10179577 · Cited by 1

Neural Semantic Video Analysis

Mohammadi, H., Firoozi, T., & Gierl, M. J. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch068

A teacher action recognition model based on pre-trained language and video model

Luo, S., Zhou, J., Wen, X., & Li, H. (2023) · International Conference on Education Technology and Computer · doi:10.1145/3629296.3629351

Action Recognition via Fine-Tuned CLIP Model and Temporal Transformer

Yang, X., Fu, Y., & Liu, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-50075-6_39

Learning with few labels for skeleton-based action recognition

Yang, S. (2023) · doi:10.32657/10356/173603

Towards robust deep learning models against corruptions

Yi, C. (2024) · doi:10.32657/10356/173647

Attr4Vis: Revisiting Importance of Attribute Classification in Vision-Language Models for Video Recognition

Zarichkovyi, A., & Stetsenko, I. V. (2024) · International Journal of Computing · doi:10.47839/ijc.23.1.3440

Modality Mixer Exploiting Complementary Information for Multi-Modal Action RecognitionPreprint

Lee, S., Woo, S., Nugroho, M. A., & Kim, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4866146

Cross-modal Short Video Recommendation

Wan, Z., Xu, Y., Yang, M., & Hua, X. (2023) · International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT) · doi:10.1109/mvipit60427.2023.00025

Dynamic Temporal Resolution Transformers

Wang, W., & Zhang, L. (2024) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl62147.2024.10603470

Temporal Transformer Encoder for Video Class Incremental Learning

Kurpukdee, N., & Borş, A. G. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647854

MST-Adapter: Multi-Scaled Spatio-Temporal Adapter for Parameter-Efficient Image-to-Video Transfer Learning

CHANG, C., Lu, T., & Feng, Y. (2024) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2024eap1080 · Cited by 1

Optimizing Factorized Encoder Models: Time and Memory Reduction for Scalable and Efficient Action Recognition

Gowda, S. N., Arnab, A., & Huang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72684-2_26

Improving Video Representation of Vision-Language Model with Decoupled Explicit Temporal Modeling

Liu, Y., Zhang, W., Chen, S., & Zhang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8511-7_37

Multi-teacher Invariance Distillation for Domain-Generalized Action Recognition

Shin, J., Maiti, A., Zou, Y., & Choi, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78110-0_8

Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use

Cho, J., Kim, S., Oh, C.-M., & Park, J. (2024) · Applied Sciences · doi:10.3390/app15010198

Description Attribute-Enhanced Spatio-Temporal Zero-Shot Action Recognition

Kim, Y., Kim, H.-J., & Lee, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-97-8705-0_20

MDAF: Enhancing Temporal Dynamics in CNNs for Action Recognition

Wu, D., Zhang, H., Cao, Y., Du, R., & Biekezat, B. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10865041

Enhanced Pose Recognition: Integrating Efficient Attention and Convolution in PoseFormer

Yuan, X., & Zhang, J. (2025) · International Conference on Sensors and Information Technology (ICSI) · doi:10.1109/icsi64877.2025.11009838

A Study of Temporal Contextual Semantic Enhanced Fusion Module for AnomalousVideo Detection

Zhang, Y., Yang, B., & Huang, Z. (2024) · Asian Conference on Artificial Intelligence Technology (ACAIT) · doi:10.1109/acait63902.2024.11022174

Cache-Adapter: Efficient video action recognition using adapter fine-tuning and cache memorization technique

Lu, T., & Chang, C. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104543

Midframe-centric token merging for efficient video transformer

Zhang, Q., Yang, Z., Shao, M., & Liang, H. (2025) · Cluster Computing · doi:10.1007/s10586-025-05478-8

Are Attention Maps Richer than we Imagined for Action Recognition?

Agrawal, T., Ali, A., Dantcheva, A., & Brémond, F. (2025) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss65446.2025.11149978

Rank-based Multi-objective Approximate Logic Synthesis via Monte Carlo Tree Search

Ye, Y., Hu, X., Liu, Y., Xu, P., Gong, Y., Chen, T., Yan, H., Yu, B., & Shi, L. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11132589

Toward an Effective Action-Region Tracking Framework for Fine-Grained Video Action Recognition

Sun, B., Wang, Y., Ma, X., Wang, Z., Lu, K., & Wang, Z. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3602089 · Cited by 1

Deep Learning for Regular Raster Spatio-Temporal Prediction: An Overview

Capone, V., Casolaro, A., & Camastra, F. (2025) · Information · doi:10.3390/info16100917

Dual-Stream Cross-Attention in Spatiotemporal Adaptation for Video Action Recognition

Li, M., Zhang, C., & Zheng, X. (2025) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi65680.2025.11201223

The Motion in the Details: Adapting CLIP for Action Recognition via Dual-prompt Guidance

Sun, L., Xu, X., Zhu, D., & Li, J. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209870

Research on Video Abnormal Behavior Detection Based Swin Transformer

Luo, Q., & Wang, Z. (2025) · International Conference on Computer Network, Electronic and Automation (ICCNEA) · doi:10.1109/iccnea66167.2025.11212067

UniAlign: A Universal Cross-Modality Knowledge Alignment Framework for Fine-Grained Action Recognition

Wang, Y., Sun, B., Li, H., Ma, X., Wang, Z., & Wang, Z. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3632670

Mask-Aware Kernel Learning for Action Recognition

Wu, K., Zhu, C., Xie, Z., Shao, K., & Guo, D. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3651108

NAS Just Once: Neural Architecture Search for Joint Image-Video Recognition

Casarin, S., Escalera, S., & Lanz, O. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00667

A Hybrid Deep Learning Approach for Human Action Prediction and Team Coordination in Sports

Edupuganti, B., Cheripally, R., Chukka, A., Joshi, Y. C., & M.A.Jabbar (2025) · IEEE Global Conference on Wireless Computing and Networking (GCWCN) · doi:10.1109/gcwcn66157.2025.11448499

Intelligent crime detection and behavioral pattern mining : a comprehensive study

Abdallah, R. (2023) · doi:10.70675/3634e53az3984z49b2za8a7z7f03e978c105

Hierarchical graph attention networks with spatio-temporal class tokens for distributed audio-visual event classification

John, V., & Kawanishi, Y. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21512-2

TAML: Task-Aware Metric-Driven Meta Learning for Few-Shot Action Recognition

Wu, W., Zhang, Y., Zheng, S., Zhu, X., Chen, Y., & Dang, Y. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11463304

Aerial Video Action Recognition with Pretrained Vision-Language Model

Zhang, Y., Chang, Y., Cao, Z., Shi, X., Song, Y., Wang, F., & Yang, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462828

RobAVA: A Large-Scale Dataset and Baseline Towards Video Based Robotic Arm Action Understanding

Sun, B., Wang, N., Ma, X., Zou, A., Lu, Y., Fan, C., Wang, Z., Lu, K., & Wang, Z. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01298

$\text{CO}_{2}$-Net: A Physics-Informed Spatio-Temporal Model for Global Surface $\text{CO}_{{2}}$ Reconstruction

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