The DOI matches its Crossref record; 73 of the 76 references it cites were found in a registry or an index.
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Open accessAbstract
Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K benchmark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code, models and data can be found in https://reself.github.io/MovieChat.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“By making plans and structured environment representations the readable and writable core of working memory, agents can maintain goal consistency and revise strategies robustly against perception failures (Song et al., 2023).”
From Memory in the Age of AI Agents (2025) · cites this paper as Song et al., 2023
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
Human Memory: A Proposed System and its Control Processes
Atkinson, R. C., & Shiffrin, R. M. (1968) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60422-3 · Cited by 7,216
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
ViViT: A Video Vision Transformer
Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., & Schmid, C. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00676 · Cited by 2,767
Llama 2: Open Foundation and Fine-Tuned Chat Models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.09288 · Cited by 2,604
Self-citationLiu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00320 · Cited by 2,294
MSR-VTT: A Large Video Description Dataset for Bridging Video and Language
Xu, J., Mei, T., Yao, T., & Rui, Y. (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.2016.571 · Cited by 1,888
Li, J., Li, D., Xiong, C., & Hoi, S. (2022) · arXiv · doi:10.48550/arxiv.2201.12086 · Cited by 1,741
Flagged first, then by citations
3 of the 76 references couldn’t be matched to a known paper.
Research works printed but not linked: 10
- 2023, Meet claude
- Awad, 2018, Trecvid 2018: Benchmarking video activity detection, video captioning and matching, video storytelling linking and video search
- 10.1109/ICCV48922.2021.00175 · DOI · Paper page
- Chiang, 2023, Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.
- Driess, 2023, Palme: An embodied multimodal language model.
- 10.1007/springerreference_9081 · DOI
- 2021, Gpt3.5, 2021.
- 2023, Gpt-4 technical report.
- Su, 2023, Bert position encoding.
- Taori, 2023, Stanford alpaca: An instruction-following llama model
Cited by
Sorted by citations
Items 101–180 of 180
VFE: A large-scale video future event description dataset for evaluating video temporal prediction
Lai, C., & Wang, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06547-6
CITR: Efficient Long Video Understanding Needs Causal Importance
Yuan, Z., Li, J., Li, Y., Huang, Y., Chen, C., Wang, S., & Gou, Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755307
Ancarani, E., Tores, J., Sun, R., Sassatelli, L., Wu, H.-Y., & Precioso, F. (2025) · doi:10.1145/3746277.3760415
AS-Memory: Adaptive Sparse Memory Meeting Video-Language Models
Wang, B., Song, H., Dang, J., Shen, F., Zhang, H., Wang, L., Xie, M., Liu, J., & Weng, J. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210118
Through the Theory of Mind’s Eye: Reading Minds with Multimodal Video Large Language Models
Chen, Z., Wang, T., Wang, Y., Kosiński, M., Zhang, X. D., Fu, Y., & Li, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229224 · Cited by 10
Time-IC: Empowering MLLM with Interleaved Context for Temporal-Sensitive Video Understanding
Zhao, H., Huang, P. F., Yan, R., & Li, Z. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3771066
VidCog: Empowering LLM with Long Video Understanding via Human-like Temporal Cognitive Loop
Wu, Y., Yang, X., & Xu, C. (2025) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3743093.3771043
Vision-Language Memory for Spatial Reasoning
Liu, Z., Du, Y., Fu, T., Su, S., Ho, C., & Wang, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37356-4_17 · Cited by 6
Wang, X., Lan, X., & Zhu, W. (2025) · Springer eBooks · doi:10.1007/978-3-031-94837-4_1
It’s Just Another Day: Unique Video Captioning by Discriminitive Prompting
Perrett, T., Han, T., Damen, D., & Zisserman, A. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02664-4
Seeing Through Words: A Zero-Shot Multimodal Audio Description System with Foundation Models
Mocanu, B., & Țapu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-14495-9_7
Momentor++: Advancing Video Large Language Models With Fine-Grained Long Video Reasoning
Li, J., Gao, M., He, X., Tang, S., Zheng, W., Xiao, J., Wang, M., Chua, T., & Zhuang, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3656169
SegCL: Segmented Reasoning with Global Visual-Audio Knowledge for Complex Long Video Understanding
Yang, S., Lü, N., Wang, B., Li, Y., & Cui, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5567-3_3
Towards Effective Long-Video Event Prediction via Multi-level Event Semantics Mining
Peng, B., Lyu, Y., Qin, P., & Xu, T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6950-2_14
Yang, J., Ma, M., Wang, P., Li, Y., Pei, Z., Yao, C., & Guo, L. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114325
Jung, J., Lee, S., Yoo, J., Yun, G., & Lee, K. J. (2026) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc49663.2026.11409158
EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning
Yu, C., Zhu, X., Du, C., Yu, P., Zhai, W., Cao, Y., & Zha, Z.-J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37335-9_36 · Cited by 1
Sparrow: Data-Efficient Video-LLM With Text-to-Image Augmentation
Yin, S., Fu, C., Zhao, S., Ge, C., Yang, Y., Dai, Y., Luo, Y., Xu, T., Shan, C., & Chen, E. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3673542 · Cited by 5
SELongVLM: Empowering Long Video Language Models With Self-Corrective Clip Selection
Zhang, K., Yang, Z., Han, M., Zhuge, Y., Hao, H., Li, C., Li, Z., & Chang, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3673141
Liu, Y., Lyu, Y., Gao, D., Liang, Z., Tang, W., Mu, S., Yang, X., & Mu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37383-0_33
Leveraging Large Language Models for Twitch Video Clip Understanding and Summarization
Lindroos, J., Koskimaa, R., Peltonen, J., Välisalo, T., & Toivanen, I. (2026) · International GamiFIN Conference (GamiFIN) · doi:10.1145/3789624.3789641
ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models
Min, T. D., Roy, S., Lathuilière, S., Ricci, E., & Mancini, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37235-2_30
Toward Semantic Enriched Video Description via Dynamic Segmentation and Keyframe Enhancement
Zhang, J., Zhang, J., Luo, Y., Zheng, Y., & Wang, W. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11437849
HiSum: A Hierarchical Framework for Long Video Summarization with Temporal Enhancement
Lin, J., Zhang, C., Shao, W., Zhu, L., Qian, Y., & Li, J. (2026) · International Conference on Consumer Electronics · doi:10.1109/icce67443.2026.11449786
PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
Li, S., Zhao, Z., Deng, H., Ma, Z., Tian, S., Liu, Z., Hu, Y., Wu, H., Dong, Y., Liu, B., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37553-7_14
Rewind-Augmented Generation for Long Video Understanding
Huang, J., Chen, L., Liang, T., Zeng, X., Kong, M. C., Li, Y., & Zhu, Q. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3679059
STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding
Kim, J., Lee, H., Rehg, J. M., Kim, M., & Ro, Y. M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37602-2_3 · Cited by 4
Free2frame: A Training-Free Framework for Video Understanding with Memory Boosting
Lang, S., Sun, P., Jiang, H., Zhu, S., Zhao, H., Yang, L., & Zhang, H. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462196
Seeing Fast and Slow: Learning the Flow of Time in Videos
Wu, Y., Luo, R., Zhu, J., Tu, T., Farhadi, A., Wallingford, M., Wang, Y.-C. F., Marschner, S., & Ma, W.-C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37335-9_29 · Cited by 1
Bringing RNNs Back to Efficient Open-Ended Video Understanding
Xu, W., Song, E., Chai, W., Wen, X., Tian, Y., & Wang, G. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02177
Self-citationChen, Y., Wang, J., Zhang, Z., Yi, J., Zhang, X., Zou, Y., Cai, Z., Yuan, J., Li, X., Yang, H., et al. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00413
EvoCap: Enhancing video captioning via self-evolving video-LLMs with knowledge consolidation
Hu, Y., Wu, M., Yu, J., Chen, Y. P., & Yang, W. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116075
Cambrian-P: Pose-Grounded Video Understanding
Yang, J., Zhao, Z., Pan, X., Yang, S., Zhang, J., Xu, H., Li, S.-W. D., & Xie, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37252-9_18 · Cited by 7
Linear Scaling Video VLMs for Long Video Understanding
Eyzaguirre, C., Wu, J., & Niebles, J. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37369-4_13
Temporal Hierarchies and Knowledge Graphs for Multi-Modal Video Language Understanding
Om, H., Attri, A., & Sharma, A. K. (2026) · International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC) · doi:10.1109/icisessc68634.2026.11542588
Wu, Z., Liu, Z., Chen, A., Zhang, J., Li, R., Ge, H., Wang, Z., Xiao, C., & Liang, C. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02898-w
Reflective Cross-Granularity Grounding with Preference Optimization for Long Video Understanding
Feng, W., Wang, X. Y., Chen, H., Zhan, Y.-W., Song, Z., Huang, B., Zheng, K., & Zhu, W. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810717
REAP: Relevance-Guided Evidence-Unit Keyframe Extraction for Long Video Question Answering
Yang, M., Qu, H., & Zhao, J. Z. (2026) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis70043.2026.11572599
VisReflect: Latent Visual Reflection for Fine-Grained Perception in Long Visual Context
Shen, X., & Elhoseiny, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37038-9_36
Bridging VideoQA and Video-Guided Agentic Tasks via Generalized Keyframe Extraction
Fan, S., Liu, Q., Yin, R., Guo, M.-H., & Yang, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37362-5_14
LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension
Kato, S., Miyanishi, T., Kurita, S., Ukai, M., Inoue, N., & Chu, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37261-1_18
Visual Calibration for Text-Guided Video Summarization
Wu, Y. M., & Li, B. (2026) · International Conference on Signal Image Processing and Communication (ICSIPC) · doi:10.1109/icsipc69751.2026.11584129
Temporal Instruction Tuning for Video Large Language Models
Deng, W., Zheng, A., Zhao, Y., Wang, T., Qi, X., & Ngai, E. C. ‐. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2480-7_29
MVSCbench: A Benchmark for Symbolic and Cultural Understanding in Music Videos
Gao, S., & Chang, B.-H. (2026) · Creativity and Cognition · doi:10.1145/3803784.3816841
Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data
Gabeff, V., Maquignaz, B., Shan, J., Mamooler, S., Sümbül, G., Costelloe, B. R., Tuia, D., & Mathis, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37574-2_3
StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description
Hahm, S. H., Dinh, M. T., & Jin, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37435-6_2
Chen, H., Wang, X., Li, G., Zhou, Y., Chen, Y., Jia, J., & Zhu, W. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809757 · Cited by 4
Cloud and Edge Systems for Large AI Models
Yuan, H., Bi, J., Zhou, M., & Buyya, R. (2026) · Internet of things · doi:10.1007/978-3-032-18400-9_15
Zhang, X., Yuan, J., Yang, B., Liu, X., Zhang, Q., Wang, Y., Li, Z., & Zhang, H. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3715157
TinyMem: Condensing Multimodal Memory for Long-Form Video Action Detection
Tian, R., Dai, Q., Han, H., & Wu, Z. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3718272
CoZSR-VAD: Contextual Zero-Shot Reasoning for Video Anomaly Detection
Wani, M. U., Atito, S., Nandam, S. R., Kittler, J., & Awais, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31920-3_46
EchoVision: A Decoupled Audio-Visual Retrieval Framework for Video Question Answering
Sarkar, S., Chakraborty, J., Nandi, S., Samui, S., & Nandi, S. (2026) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom71105.2026.11622962
Towards an Efficient and Unified Strategy for Video Understanding Applications
Kumar, R., & Channappayya, S. S. (2026) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom71105.2026.11623000
VideoExplorer: Advancing Long-Horizon Video Understanding via Hierarchical Orchestration
Yuan, H., Liu, Z., Zhou, J., Qian, H., Shu, Y., Sebe, N., Wen, J.-R., & Dou, Z. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817705
Advancing Multimodal Agent Reasoning with Long-Term Neuro-Symbolic Memory
Jiang, R., Wang, J., Zhao, G., Luo, C., Wang, K., & Zhang, W. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817643
Long Story Short: Story-level Video Understanding from 20K Short Films
Ghermi, R., Wang, X., Kalogeiton, V. S., & Laptev, I. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02940-x · Cited by 22
Large Vision Models (LVMs) for Video Intelligence
Bhatti, U. A., Gulzar, Y., Ghadi, Y. Y., & Bhatti, M. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0965-9.ch008
Task-Adaptive Calibration for Multimodal Long-Context ExtensionPreprint
Li, X., Guo, Z., & Liu, R. (2026) · Research Square · doi:10.21203/rs.3.rs-10769418/v1
A Survey of Multi-Model Collaboration in Video Understanding
Chen, Y., Zhang, J., Zhang, L., Liu, C., Gao, R., Lu, Z., Qi, J., & Lei, Q. (2026) · Data · doi:10.3390/data11090230
STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation
Hesham, S. A. S., Liu, Y., Sun, G., Yang, J., Ding, H., Geng, X., & Jiang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37020-4_26
Meng, L., Li, Q., Mao, J., Liao, P., He, Z., ZHANG, E., Wang, K., Liu, Y., Fu, H., & Jin, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36839-3_32
Training-Free Event-Aware Memory Enhancement for Streaming Video Question Answering
Song, Y. (2026) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc70586.2026.11686687
Li, Y., Yang, Y., Yu, F., Hu, J., Fang, Z., & Huang, L. (2026) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia71496.2026.11684568
Beyond Physical Safety: Vision-Language Models for Emotional Safety Reasoning in Dementia Care
Inaparthy, T., & Yuan, F. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-5190-2_8
H$$^2$$SVC: Head-Aware Heterogeneous Streaming Video Cache for Online Video Understanding
Li, H., Zhang, X., Dai, W., Wang, Y., Peng, X., He, F., Xu, H., Zheng, Z., Li, C., Zou, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37174-4_26
AdaBIMBA: Adaptive Token Compression for Long Endoscopy Video Understanding
Yung, K.-W., Mobadersany, P., Parmar, C., Chaitanya, K., Gunderson, F., Surace, L., Ghanem, L. R., Mansi, T., Cula, G. O., Standish, K., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38233-7_1
STVFocus: Query-Guided Spatio-Temporal Visual Focusing for Video LLMs
Kong, T., Park, M., & Ahn, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37135-5_18
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
Gong, S., Yan, T., Kang, C., Zheng, B., Ruan, X., Lu, H., Zhang, K., Sato, Y., & Huang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37138-6_9 · Cited by 11
Less is More: Token-Efficient Video-QA via Adaptive Frame-Pruning and Semantic Graph Integration
Wang, S., Guo, W., Chen, Z., Xu, Y., Hu, X., & Xiong, H. (2026) · doi:10.1109/cvprf71909.2026.00943
CaptAin: Caption-Driven Alignment for Bridging Modality Gaps in Partially Relevant Video Retrieval
Chen, C., Zhou, K., Wang, F., Ning, Y., Xiong, Z., Li, Y., Wen, Z., & Tan, M. (2026) · doi:10.1109/cvprf71909.2026.00596
ReFoCUS: Reinforcement-Guided Frame Optimization for Contextual Understanding
Lee, H., Kim, J., Kim, H., & Ro, Y. M. (2026) · doi:10.1109/cvprf71909.2026.00796
Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs
Chung, H., Nam, H., Kim, J., Go, H., Park, B., Kim, J., Lee, J., Ha, S., & Kim, B.-H. (2026) · doi:10.1109/cvprf71909.2026.00861
Frequency-Modulated Visual Restoration for Matryoshka Large Multimodal Models
Pan, Q., Dou, Z., & Li, S. (2026) · doi:10.1109/cvprf71909.2026.00931
V-STaR: Benchmarking Video-LLMs on Video Spatio-Temporal Reasoning
Cheng, Z., Hu, J., Liu, Z., Si, C., Li, W., & Gong, S. (2026) · doi:10.1109/cvprf71909.2026.00878
Towards Streaming Video Understanding for Embodied Scenarios
Wang, Y., Li, Z., Qian, T., Zheng, H., Wang, Z., Fu, Y., & Wang, X. (2026) · doi:10.1109/cvprf71909.2026.00903
VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs
Zheng, N., Guo, Q., Huang, J., & Zhao, F. (2026) · doi:10.1109/cvprf71909.2026.00571
Xu, Z., Wu, Z., Zhang, C., Wang, Z., Xiao, C., & Liang, C. (2026) · doi:10.1109/cvprf71909.2026.00792
Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams
Guo, Z., Man, Y., Sheng, J., Lin, B., Ahmed, A., Jiang, B., Zhang, B., Yin, M., Jin, S., Gnawali, O. D., et al. (2026) · doi:10.1109/cvprf71909.2026.00298
Training-Free Video Summarization via Hierarchical Semantic Modeling and Global Theme Constraints
Liu, L., Sun, J., Wang, Z., Tian, L., Duan, Y., Zhang, T., Weng, C., Chen, J., Cao, Y., Dang, X., et al. (2026) · doi:10.1109/aice71085.2026.11736968
Wen, S., Wang, Z., Zhang, X., Huang, L., & Wu, W. (2026) · arXiv · doi:10.48550/arxiv.2602.15329
Metadata
- Authors
- Song, E., Chai, W., Wang, G., Zhang, Y., Zhou, H., Wu, F., Chi, H., Guo, X., Ye, T., Zhang, Y., Lu, Y., Hwang, J.-N., & Wang, G.
- Affiliations
- Zhejiang University (CN)
- University of Washington (US)
- Donghua University (CN)
- Microsoft Research Asia (China) (CN)
- Hong Kong University of Science and Technology (GZ)
- ORCID
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 18221-18232
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4402702917
- Cited by
- 176
- References
- 76
- Citations by year
- 2026: 82 · 2025: 60 · 2024: 32
- FWCI
- 35.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key R&D Program of China (2022ZD0162000)
- National Natural Science Foundation of China (62106219,62206046)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- 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
- Keywords
- long video understanding, video foundation models, large language models, temporal relations
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
