Temporal Video Understanding

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23 papers in the last four weeks, up 109% on the four weeks before. 0.2% of all new papers.

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Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition
May 6, 2026Eugene Kofi Okrah Denteh, Blessing Agyei Kyem, Joshua Kofi Asamoah +1Intelligent Transportation Systems Multimodal Fusion
Civil, Construction and Environmental Engineering Department, North Dakota State University, North Dakota, United States of America
TemPose-TF-ASF: Two-Stage Bidirectional Stroke Context Fusion for Badminton Stroke Classification
May 4, 2026Tzu-Yu Liu, Duan-Shin LeeSports Video Analysis Video Action Recognition
Institute of Communications Engineering National Tsing Hua University, Hsinchu City, Taiwan · Department of Computer Science National Tsing Hua University, Hsinchu City, Taiwan
Video Active Perception: Effective Inference-Time Long-Form Video Understanding with Vision-Language Models
May 3, 2026Martin Q. Ma, Willis Guo, Aditya Agrawal +4Active Perception Efficient VLM Inference
Carnegie Mellon University · MIT
VISTA: Video Interaction Spatio-Temporal Analysis Benchmark
May 2, 2026Alejandro Aparcedo, Akash Kumar, Aaryan Garg +5VLM Evaluation Video Understanding
University of Central Florida · BITS Pilani · Ho Chi Minh City University of Science +1
High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions
May 1, 2026Yongpeng Cao, Yuji YamakawaVideo Understanding Zero-Shot Learning
Institute of Industrial Science, The University of Tokyo, Japan · Interfaculty Initiative in Information Studies, Graduate School of Interdisciplinary Information Studies, The University of Tokyo, Japan
Scaling Video Understanding via Compact Latent Multi-Agent Collaboration
May 1, 2026Kerui Chen, Jinglu Wang, Jianrong Zhang +3Multi-Agent Collaboration Long-Video Understanding
Microsoft Research Asia · Zhejiang University · Guanming Lab.
TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions
Apr 30, 2026Ce Chen, Yi Ren, Yuanming Li +5Vision-Language Models Change-Point Detection
University of Melbourne · 2HeyGen Research · 3Nanyang Technological University
$\text{PKS}^4$ :Parallel Kinematic Selective State Space Scanners for Efficient Video Understanding
Apr 29, 2026Lingjie Zeng, Hailun Zhang, Xiwen Wang +1Visual SSMs Video Action Recognition
College of Computer Science, Sichuan University Chengdu, China
Beyond Screenshots: Evaluating VLMs' Understanding of UI Animations
Apr 28, 2026Chen Liang, Xirui Jiang, Naihao Deng +2VLM Evaluation Video Understanding
University of Michigan
PushupBench: Your VLM is not good at counting pushups
Apr 25, 2026Shengzhi Li, Jiarun Chen, Karun Sharma +2VLM Evaluation Video-Language Model Evaluation
EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
Apr 25, 2026He Hu, Tengjin Weng, Zebang Cheng +5Multimodal Large Language Models Multimodal Model Evaluation
Shenzhen University Shenzhen, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen, China · Queen Mary University of London +3
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Apr 24, 2026Marco Pegoraro, Jonas Seng, Dustin Heller +2Process Mining Temporal Video Understanding
Chair of Process and Data Science, RWTH Aachen University, Ahornstr. 55, 52074 Aachen, Germany · Artificial Intelligence & Machine Learning Lab, Technical University of Darmstadt, Hochschulstr. 1, 64289 Darmstadt, Germany
Seeing Fast and Slow: Learning the Flow of Time in Videos
Apr 23, 2026Yen-Siang Wu, Rundong Luo, Jingsen Zhu +6Controllable Video Generation Temporal Video Understanding
Cornell University · University of Washington · National Taiwan University
Divide-then-Diagnose: Weaving Clinician-Inspired Contexts for Ultra-Long Capsule Endoscopy Videos
Apr 23, 2026Bowen Liu, Li Yang, Shanshan Song +6Endoscopy Medical Imaging
1The Hong Kong University of Science and Technology · 2Renji Hospital, Shanghai Jiao Tong University School of Medicine
HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration
Apr 23, 2026Yuehan Zhu, Jingqi Zhao, Jiawen Zhao +2Multi-Agent Collaboration Multimodal QA
School of Artificial Intelligence, Sun Yat-sen University, China
Exploring High-Order Self-Similarity for Video Understanding
Apr 22, 2026Manjin Kim, Heeseung Kwon, Karteek Alahari +1Spatiotemporal Reasoning Video Action Recognition
POSTECH · RLWRLD Inc. · INRIA Grenoble Rhône-Alpes
Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction
Apr 22, 2026Dali Wang, Yunyao Zhang, Junqing Yu +3Social Media Analysis Memory-Augmented Neural Networks
Huazhong University of Science and Technology Wuhan, China · La Trobe University Melbourne, Australia · Beijing Institute of Computer Technology and Applications Beijing, China
EAST: Early Action Prediction Sampling Strategy with Token Masking
Apr 20, 2026Iva Sović, Ivan Martinović, Marin OršićVideo Action Recognition Temporal Video Understanding
Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection
Apr 20, 2026Sa Zhu, Wanqian Zhang, Lin Wang +3Cross-Modal Alignment Temporal Action Detection
Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, University of Chinese Academy of Sciences, State Key Laboratory of Cyberspace Security Defense, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China · Hangzhou Dianzi University, Hangzhou, China +1
LiquidTAD: Efficient Temporal Action Detection via Parallel Liquid-Inspired Temporal Relaxation
Apr 20, 2026Zepeng Sun, Naichuan Zheng, Hailun Xia +3Liquid Neural Networks Human Activity Recognition
Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test
Apr 19, 2026Tairan Fu, Francisco Javier Santos-Martín, Javier Conde +2VLM Evaluation Industrial Inspection
A Progressive Training Strategy for Embodied Vision-Language Models to Mitigate Spatio-Temporal Hallucinations
Apr 12, 2026Xiaoda Yang, Shuai Yang, Can Wang +9VLM Adaptation VLM Hallucination
The Hong Kong University of Science and Technology · The Chinese University of Hong Kong
HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment
Apr 9, 2026Changdao Chen, Qinqiuhong Ye, Hao Chen +1Heterogeneous GNNs Hypergraph Neural Networks
School of Computer Science and Technology, Xi’an Jiaotong University Xi’an, Shaanxi, China · School Of Mechanical Engineering, Xi’an Jiaotong University Xi’an, Shaanxi, China · Xi’an Jiaotong University Health Science Center Xi’an, Shaanxi, China
An Approach to Enriching Surgical Video Datasets for Fine-Grained Spatial-Temporal Understanding of Vision-Language Models
Apr 1, 2026Lennart Maack, Alexander SchlaeferMedical VQA Surgical Video Understanding
Hamburg University of Technology, Germany
SurgTEMP: Temporal-Aware Surgical Video Question Answering with Text-guided Visual Memory for Laparoscopic Cholecystectomy
Mar 31, 2026Shi Li, Vinkle Srivastav, Shih-Min Yin +7Medical VQA Surgical Video Understanding
University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France · IHU Strasbourg, Strasbourg, France · Department of General Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan +1
Video-Oasis: Rethinking Evaluation of Video Understanding
Mar 31, 2026Geuntaek Lim, Sungjune Park, Jaeyun Lee +5VLM Evaluation Temporal Video Understanding
Sejong University, South Korea · NAVER Cloud, South Korea
CLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition
Mar 30, 2026Muhammad Osama Zeeshan, Masoumeh Sharafi, Benoit Savary +3VLM Adaptation Facial Expression Recognition
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montreal, Canada · Dept. of Computer Science, École Polytechnique, Paris, France · LIVIA, Dept. of Software and IT Engineering, ETS Montreal, Canada
A Two-stage Transformer Framework for Temporal Localization of Distracted Driver Behaviors
Mar 22, 2026Gia-Bao Doan, Nam-Khoa Huynh, Minh-Nhat-Huy Ho +3Driver Monitoring Temporal Video Understanding
Department of Information Technology FPT University Ho Chi Minh City 700000, Vietnam · Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City 700000, Vietnam · School of Computer Science and Engineering The Saigon International University Ho Chi Minh City 700000, Vietnam
Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
Feb 16, 2026Praditha Alwis, Soumyadeep Chandra, Deepak Ravikumar +1Temporal Video Understanding
Purdue University West Lafayette, IN 47907, USA
Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models
Feb 11, 2026Sethuraman T, Savya Khosla, Aditi Tiwari +12VLM Robustness Video-Language Model Evaluation
University of Illinois Urbana-Champaign, USA · Adobe Research, USA · Qualtrics, USA +6

May 6, 2026, cs.CVJ/K move · Enter open · S save

Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition

Eugene Kofi Okrah Denteh, Blessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah

Civil, Construction and Environmental Engineering Department, North Dakota State University, North Dakota, United States of America

Intelligent Transportation Systems Multimodal Fusion Temporal Video Understanding

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Accurate traffic congestion classification requires models that jointly capture roadway scene context and non-stationary traffic motion, yet most prior work treats these requirements in isolation. Vision-based methods often depend on appearance cues with standard temporal pooling, which can bias predictions toward static infrastructure, whereas signal-based approaches characterize temporal dynamics but lack the spatial context needed for scene-level localization. These complementary limitations motivate a unified framework that links motion evidence to spatial feature selection while preserving data-adaptive temporal characterization. This study therefore proposes FLO-EMD, a hybrid approach that couples motion-guided attention with empirical, data-driven temporal decomposition. Dense optical flow guides channel and spatial attention so that RGB features are refined toward motion-relevant regions. In parallel, aggregated flow statistics form compact motion traces that are decomposed using Empirical Mode Decomposition (EMD) to extract intrinsic temporal components. The resulting EMD embedding is fused with learned spatiotemporal representations to classify light, medium, and heavy congestion. Experiments on 1,050 five-second clips from four surveillance networks show that FLO-EMD achieves 97.5% overall test accuracy (weighted F1 = 0.9742), outperforming established baselines and remaining robust across diverse environmental conditions; ablation and sensitivity analyses further quantify the contributions of EMD, the number of intrinsic mode functions, and the selected motion descriptors.

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