Cited by 843Multimodal Machine Learning ApplicationsAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

The DOI matches its Crossref record; 138 of the 145 references it cites were found in a registry or an index.

Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Open access

Chen, Zhe, Wu, Jiannan, Wang, Wenhai, Su, Weijie, Chen, Guo, Xing, Sen, Zhong, Muyan, Zhang, Qinglong, Zhu, Xizhou, Lu, Lewei, Li, Bin, Luo, Ping, Lu, Tong, Qiao, Yu, Dai, Jifeng·2024·2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr52733.2024.02283

Open this paper in CoChat — free account Open PDF

Abstract

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foun-dation model (Intern VL), which scales up the vision foun-dation model to 6 billion parameters and progressively aligns it with the LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.

Integrity signals

Registry

Crossref · 10.1109/cvpr52733.2024.02283

Cites

138 of 138 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
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
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
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
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515
Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465

Show all 145 references

Research works printed but not linked: 23

Achiam, 2023, Gpt-4 technical report, arXiv preprint
Bai, 2023, Qwen tech-nical report, arXiv preprint
Bai, 2023, Qwen-vl: A frontier large vision-language model with versatile abilities, arXiv preprint
Byeon, 2022, Coyo-700m: Image-text pair dataset
Carlsson, 2022, Cross-lingual and multilingual clip, LREC, 6848
Chen, 2023, Shikra: Unleashing multi-modal 11ms referential dialogue magic, arXiv preprint
Dosovitskiy, 2020, An image is worth 16×16 words: Transformers for image recognition at scale, ICLR
Goyal, 2017, Making the v in vq a matter: El-evating the role of image understanding in visual question answering, CVPR, 6904
2023, Introducing idefics: An open reproduction of state-of-the-art visual language model
Ilharco, 2021, Open-clip. Zenodo. Version 0.1
2023, Clip benchmark: Clip-like model evaluation
Li, 2023, Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models, 19730
Li, 2019, Coco-en for cross-lingual image tagging, captioning, and retrieval, TMM, 21, 2347
Liu, 2023, Aligning large multi-modal model with robust instruction tuning, arXiv preprint
Sawada, 2022, Construction and public release of language image pretraining models in japanese, MIRU
Oquab, 2023, Dinov2: Learning robust visual features with-out supervision, TMLR
Schuhmann, 2022, Laion coco:600m synthetic captions from laion2b-en
Sun, 2024, Generative pretraining in mul-timodality, ICLR
Sun, 2023, Moss: Training conversationallanguage models from synthetic data, arXiv preprint, 7
Taori, 2023, Alpaca: A strong, replicable instruction-following model, Stanford Center for Research on Foundation Models, 3, 7
Team, 2023, Internlm: A multilingual language model with progressively enhanced capabilities
Xie, 2022, Zero and r2d2: A large-scale chi-nese cross-modal benchmark and a vision-language frame-work, arXiv preprint
Zhu, 2023, Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory, arXiv preprint

Sorted by citations

Items 601–700 of 843

HOLO: Holistic Lightweight Optimization for Scene Understanding with Auto-Annotation and Multimodal Learning
Hu, X., Yan, X., Wang, H., Wei, G., & Wang, Z. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00745
FinePruner: Unbiased Attention-Head-Level Fine-Grained Token Reduction for Efficient Inference of Large Vision-Language Models
Wang, Z., Zheng, X., & Peng, Y. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3687073
Context-Aware Visual Multi-turn Conversation Generation from Wikipedia and Wikidata
Shbita, B., Li, P., & Gentile, A. L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25159-6_19
A Multi-Source Pipeline for Extracting Traditional-Style Chinese Melody Data from Symbolic Files and Score Images
Zhou, X., Huang, Y., Han, S., & Bai, J. (2026) · Computers · doi:10.3390/computers15050298
ArtScale: autoregressive super-resolution for art paintings via multi-scale vision-language guidance
Ji, Z. H. O. U., Tian, Q., Hu, J., & Tan, Z. (2026) · npj Heritage Science · doi:10.1038/s40494-026-02583-8
Leveraging Generative AI for Primary School Mother Tongue Language Learning: A Scalable and Adaptive AI-Driven Approach
Soh, D. C. L., Atmosukarto, I., Rong, T., McLoughlin, I. V., Hao, L. J., Sirajudeen, M., Kan, J., Pranav, T., Zhang, B., Latiff, N. A. A., et al. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16157-4_3
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
WinTok: A Win-Win Hybrid Tokenizer via Decomposing Visual Understanding and Generation with Transferable Tokens
Guo, Y., Zhuang, S., Huang, Z., Fu, C., Li, C., Lyu, J., & Wang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37602-2_36 · Cited by 1
REC-RL: Referring Expression Counting via Gaussian and Range-Based Reward Optimization
Liu, H., Teng, Y., Bai, K., Qi, P., Yan, H., Li, L., & Feng, J. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630559
Prompt Design and Quality Analysis for Automated Captioning of Autonomous Driving Data in an On-Premises VLM Environment
Lim, S., Kim, J., Lee, J.-W., & 최정단 (2026) · The Journal of The Korea Institute of Intelligent Transport Systems · doi:10.12815/kits.2026.25.2.179
Evidence-Grounded Temporal Reasoning in Operating Room Long Video Understanding
Li, Y., Li, J., & Heng, P.-A. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515743
Resolution-Aware Knowledge Distillation from Vision-Language Models for Efficient OCR: Diagnosing and Correcting Teacher Label ContaminationPreprint
Ou, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9689250/v1
Fine-Grained Attention Enhancement for Mitigating Hallucinations in LVLMs
Yang, J., Yao, H., Chen, X., & Jiang, S. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-9493-1_9
SparKV: Overhead-Aware KV Cache Loading for Efficient On-Device LLM Inference
Liu, H., Zhai, L., Wang, J., Fang, Z., Chen, J., & Huang, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3695763
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
Beyond the Brush++: a flexible pipeline for fully automated generation of realistic inpainted images
Bertazzini, G., Albisani, C., Baracchi, D., Shullani, D., & Piva, A. (2026) · Journal on Information Security · doi:10.1186/s13635-026-00237-7
Embodied AI
Wang, X., Feng, T., Liu, H., & Zhu, W. (2026) · Springer eBooks · doi:10.1007/978-981-95-7750-7_1
Docs2Synth: A Synthetic Data Tuned Retriever Framework for Documents Understanding
Ding, Y., Sun, Q., Wu, P., Li, S., Luo, S., & Liu, W. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774905.3793117
Physical plausibility reasoning via HCM-GRPO: Empowering compact model for superior performance
Hu, Z., Sun, Z., Wei, Y., & Yu, L. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114108
NTLPCBLM: Non-motorized traffic lane participants crossing behavior language model
Zhang, B., Gao, Y., Yan, J., Wu, X., & Zhang, K. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261447205
Generative anomaly detection: a comprehensive review of modeling principles, advances, and future opportunities
Zhu, J., Fan, Y., Han, G., Shi, X., Deng, F., & Chen, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11591-w
Recognizing Co-speech Gestures in-the-Wild
Hegde, S. B., Prajwal, K. R., & Zisserman, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37350-2_20
ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis
Jin, Z., Luo, Y., Zhang, Y., Cui, Z., Wei, Y., Liu, X., Zeng, X., & Zhang, Q. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02010-1
Self-citation
Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions
Matsuo, H., Nishio, M., Fujimoto, K., Deperrois, N., Matsunaga, T., Nooralahzadeh, F., Krauthammer, M., & Murakami, T. (2026) · npj Health Systems · doi:10.1038/s44401-026-00087-y · Cited by 1
Bridging the Semantic Void: A Dual-branch RAG Framework to Enhance GUI Component Description for BVI Users
Wu, Y., Zhou, S., Wang, Z., Li, L., & Bu, J. (2026) · International Web for All Conference (W4A) · doi:10.1145/3800424.3800448
Deciphering Object Concepts: Hierarchical Cross-Modal Relational Reasoning for Mining Object-Attribute-Affordance Associations
Wang, Y., Yang, M., Zhu, Y., Yan, J., Fang, F., Yang, X., & Deng, C. D. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3700158
OCC-MLLM-V1: Occlusion reasoning with commonsense-guided Multi-modal LLM based agent via internal Chain-of-Thoughts (CoTs)
Wang, C., He, Q., Pei, J., Xia, L., Liu, J., Li, B., & Di, X. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104828
Self-citation
Bridging human insight and automation: improving alt text generation with human-curated contextual data
Droutsas, N., Spyridonis, F., Daylamani-Zad, D., Glass, P. E., & Ghinea, G. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2678381
A hierarchical evaluation framework for security and trustworthiness in Large Vision-Language Models
Feng, X., Feng, M., Zhang, N., Gu, T., Chang, L., & Bin, C. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114161
MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Agentic Hierarchical Graph Memory
Chen, C., Gan, G., Ji, K., Zhang, C., Yang, Z., Yao, G., Chen, H., Chen, J., Yuan, Y., & Shen, C. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-main.270 · Cited by 7
Semantic Region-Guided Transferable Attacks on Vision-Language Pretraining Models
Pan, J., Chen, W., Chen, W., & Huang, L. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3701225
SceneVLP: Augmenting Vision-Language Pre-Training Models for Video Action Recognition With Video Scene Graphs
Liu, L., Li, G., Xie, Q., Wang, Z., Kuang, L., Xia, Y., & Yin, J. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3701841
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Huang, Y., Thede, L., Mancini, M., Xu, W., & Akata, Z. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02865-5 · Cited by 2
Cibic: Pixel-free foundation model for robust corrupted image bitstream captioning
Liang, J., Wu, K., Hu, X., & Liu, T. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114238
Revisiting InternVL: A Systematic Technical Framework for Building Powerful Open-Source Vision-Language Models
Chen, Z., Wang, W., Zhu, J., Wang, X., Liu, Y., Liu, Z. T., Luo, G., Sun, P., Su, W., Tian, H., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3702168
Self-citation
ED-Former: Efficient dehazing transformer with Attention-Adaptive Feed-Forward Network
Chen, J., He, Y., (91924), X. W., Guo, Z., Chen, J., Rao, Z., & Xiang, Y. (2026) · Signal Processing Image Communication · doi:10.1016/j.image.2026.117634
Toward fine-grained construction safety inspection via vision-language grounded reasoning
Luo, X., Li, C., Gu, B., & Fang, D. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104870
Extending a Vision–Language Model with Audio Understanding: Introducing Qolda-AVL for the Kazakh Language
Arystanbekov, B., Maxutov, A., Nurimanov, A., & Varol, H. A. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10060192
TAVEN: Task-driven Adaptive Viewpoint Exploration for Training-Free 3D Spatial Reasoning and Understanding
Jiang, S., Yuan, Z., & Zhao, N. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810869
Stillness is Redundant: Motion-Aware KV Cache Retrieval for Efficient Video Understanding
Li, J., & Zheng, H. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810837
Synergizing Agentic Data Generation and Efficient Representation Learning for Medical Cross-Modal Understanding
Cheng, L. (2026) · International Conference on Multimedia Retrieval · doi:10.1145/3805622.3810827
UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer
Wang, S., Li, L., Chen, Y., Gao, R., Teng, Y., & Wang, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37422-6_17 · Cited by 2
Towards Unconstrained Human-Object Interaction
Tonini, F., Conti, A., Vaquero, L., Beyan, C., & Ricci, E. (2026) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg67764.2026.11557036
LGVSC: A Large-Model-Driven Generative Video Semantic Communication Framework
Ma, Y., Yin, H., Qiao, L., Sun, S., Gao, Z., Xu, Y., & Zhang, W. (2026) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2026.3705309
Improving Reasoning in Vision-Language Models via Perception Verified Self-training
Sharma, S., Gupta, S. K., & Thakur, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37592-6_19 · Cited by 1
LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement
Guan, T., Wang, H., Shen, W., & Yang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37211-6_37 · Cited by 1
A domain-specific large language model for surface-crack diagnosis in continuous casting slabs via multimodal data engineering and QLoRA fine-tuning
Wang, X., Zhang, C., Ding, J., Zhang, L., & Wang, Y. (2026) · Progress in Natural Science Materials International · doi:10.1016/j.pnsc.2026.08.001
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
Chen, B., Cai, Y., Luo, Y., Zhang, Y., Yin, J., & Chen, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37517-9_13
Self-citation
From Figures to Datasets: Vision-Language Models for Quantitative Data ExtractionPreprint
Gabay, L. C. S., Costa, D. P., Mendes, P. S. F., & Barata, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10090500/v1
FeVOS: Foresight Expression Video Object Segmentation
Lan, K., Ying, K., & Ding, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37314-4_19 · Cited by 1
ViQ: Text-Aligned Visual Quantized Representations at Any Resolution
Yu, X., Liu, Z., Yang, Z., Dong, Y. H., Qian, S., Lu, J., Hu, H., & Rao, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37356-4_30 · Cited by 2
GeMoE: Gating Entropy Is All You Need for Uncertainty-Aware Adaptive Routing in MoE-Based Large Vision-Language Models
Cai, C., Weng, M., Li, J., Jiang, Y., Yang, L., Qin, Z., & Li, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37369-4_26
MVPruner: Dynamic Token Pruning for Accelerating Multi-view Vision-Language Models in Autonomous Driving
Yang, N., Liu, Z., Zhang, L., Xie, S., Wang, Y., Zhou, W., & Zhao, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37595-7_20
Vision foundation models for engineering document intelligence and manufacturing inspection: a survey
Pagidi, K. R., & Gardas, A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11622-6
NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript Understanding (Palestine Memory–Omar Al-Saleh Memoir)
Hamoud, H. K., Chamseddine, A. A., Shalash, B. M., Abid, F. B., Jarrar, M., Chakra, C. A., Ghanem, B., & Zaraket, F. A. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3iakmct86er7
Preserving Language Capabilities in Vision-Language Models via Representation Regulation
Chen, Z., Tao, J., & Zeng, Z. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1210
Self-citation
Real-Time Video Analytics With Adaptive Continual Learning via Hybrid Edge and Cloud Collaborations
Zhao, K., Liao, Z., Zeng, L., Hong, G., Ouyang, T., Zhou, Z., & Chen, X. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3709168
Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation
Chen, Y., Zhu, J., Yu, Y., Shi, Y., Naghshbandi, H., Liu, J., Koh, A. S.-M., Fang, F., Ong, K. E., & Yeo, S. C. K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_54
When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise
Shin, P. W., Sridhar, A. N., Devarapalli, L. S., Zhang, R., Sampson, J., & Narayanan, V. (2026) · Workshop on Advances in Language and Vision Research (ALVR) · doi:10.18653/v1/2026.alvr-main.14
DualFact+: A Multimodal Fact Verification Framework for Procedural Video Captioning
Oguz, C., Hamidullah, Y., Genabith, J. V., & Ostermann, S. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1912
GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning
Zhang, J., Yan, Y., Zheng, K., Zou, X., Dai, S., & Hu, X. (2026) · Workshop on Advances in Language and Vision Research (ALVR) · doi:10.18653/v1/2026.alvr-main.11
MV-GEL: Language-Driven Multi-view Geometric Entity Localization on Meshes
Bali, K., & Aydin, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37026-6_33
Inject to Heal: Alleviating hallucination in LVLMs via Context Embedding Injection
Fazli, M., Wei, B., & Zhu, Z. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.2048
Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
Wang, X., Yang, X., & Shen, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37026-6_31
Seek to Segment: Active Perception for Panoramic Referring Segmentation
Tang, S., Hu, S., Shuai, X., Ding, H., & Jiang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37044-0_6 · Cited by 1
ReQuest: Rethinking-Based Question-Aware Frame Selection for Long-Form Video QA
Kim, M., Yun, S., Kim, Y. T., Moon, J., Choi, J. H., & Kim, S. T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37235-2_23 · Cited by 1
Teaching Vision-Language-Action Models What to See and Where to Look
Yang, Y., Chen, C., Tan, Z., Wang, Y., Feng, Z., Liu, C., Sheng, K., Zhang, J., Yang, L., Zhang, B., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37356-4_18 · Cited by 1
Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning
Zhao, C., Ma, J., Huang, Q., Fan, T., Li, H., Kang, Z., Wei, X., Yang, J., & Tai, Y. Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37490-5_4
CoTGen: Chain-of-Thought Enhanced Reasoning for Cross-Scenario Industrial Anomaly Detection
Pang, F., Ouyang, Z., Yang, A., Zeng, P., & Wang, Z. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582291
Find what you missed: Causal recovery for visual tokens in vision-language models
Qian, T., Wang, Q., Gao, S., & Yu, H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116589
A Spatial–Channel Aware Adaptive Connector for Multimodal Multi-Layer Feature Fusion
Zhang, H., Zhao, C., Wang, F., Wu, X., Ma, K., Zhu, H., Liang, S., & Zhang, P. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582221
PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving
Tang, P., Wang, G., Ren, X., Wang, Z., Zhao, G., Feng, B., & W, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37281-9_28
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
Dong, W., Wang, Z., Zhang, S., Sun, K., Li, B., Xie, G.-S., Shan, C., & Zhao, F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37013-6_21 · Cited by 1
Self-citation
DGSeg: Dynamic Gating of Semantic–Spatial Guided Predictions for Reasoning Segmentation
Zeng, R., Cao, S., Zhang, L., & Liu, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37038-9_13
IRIS: An Intelligent Vision-Language System for Ocular Surface Diseases via Topic Tree and Scene-Driven VQA Generation
Wei, H., Qi, W., Dai, D. Z., Zhang, M., & Yuan, W. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38059-3_22
HCSU: A Dataset and Benchmark for Fine-Grained Historical Calligraphy Style Understanding
Yao, Y., Liu, Y., & Ye, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37461-5_5
QSVideo: Query-Conditioned Semantic Temporal Retrieval for Video Understanding
Ao, W., Wang, L., & Boddeti, V. N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37490-5_9
Transferability Between Understanding and Generation in Unified Multimodal Models
Kang, J., Yoon, H., Jung, J., Min, J., Jeon, M., Hwang, B., Jung, S., & Kim, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37132-4_35 · Cited by 4
SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts
Feng, H., Wei, H., Wang, H., Li, S., Li, C., & Wu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37574-2_33
A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation
Wang, L., Sheng, K., He, Z., Li, J., Qin, Y., Dai, H., Wang, X. F., Yang, J., Yan, Q., Liu, C., et al. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3711284
6G-Oriented Semantic Extraction in Semantic Communications: From DL to Large AI Models
Wei, Y., Chen, X., Yang, Z., Shi, J., Jiang, H., Pan, C., Shu, F., & Liu, J. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3712130
Towards scalable and context-aware multimodal interactive video retrieval
Tran, B. D., Le, K., & Ngo, T. D. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02459-8
Att-VCD: Mitigating Hallucination in MLLMs via Attention-Guided Visual Contrastive Decoding
Zhang, C. T., Wang, Q., Wang, Y., Zhang, L., Fu, Z., & Zhang, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3432-5_43
VPCC: A Collaborative Framework Between Vision-Language Models and Specialized Small Models for Joint Industrial Defect Detection and Classification
Yin, B., & Wen, M. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3432-5_45
ACE-HVD: A Multi-Agent Collaboration Approach for Interpretable Hate Video Detection
Li, K., Zhong, S., Fang, M., Hong, R., Lang, J., & Zhou, F. (2026) · International Conference on Communications · doi:10.1109/icc59461.2026.11587024
Benchmarking Dynamic Affective Reasoning: A Viewer-Centric Video Emotion Dataset
Zhang, Z., Song, P., Hu, J., Jia, J., Yang, X., & Chang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37038-9_15 · Cited by 3
BioRxnReasoner: Multi-agent Reasoning for Biochemical Reaction Diagram Question Answering
Zhou, R., Miao, Q., Zhang, J., Zhang, B., Zhou, S., & Wei, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3719-7_29
LIT-Bench: A Multi-Level Evaluation Benchmark for Vision-Language Models in Intelligent Transportation Systems
Zhang, G., Fang, S., Cui, Y., Zhang, J., Zheng, L., & Hang, P. (2026) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms69701.2026.11602271
A Comprehensive Survey on Multimodal Large Language Models for Educational Visual Question Answering: Towards Vietnamese Reasoning Systems
Le, X. T., Vo, Q. N. T., Tran, T. H. M., Nguyen, T. T., & Quan, T. T. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2885-0_8
Evidence-Grounded Oracle Bone Script Understanding: OBB Benchmark and OBS-PRTF Framework
Liu, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3510-0_20
Decoupled visual processing: Efficient multimodal adaptation via modality-specific transformer substitution
Feng, M., Wen, Z., & Tao, J. (2026) · iFuture · doi:10.26599/if.2026.9710005
Display-Recaptured Document Image Detection Under Texture Interference
Li, P., Zheng, R., Chen, C., Chernyshova, Y. S., Nikolaev, D., Tan, S., Arlazarov, V. V., & 夏来群 (2026) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2026.3729152
Learning generalizable representations across Heterogeneous Acquisition Environments for Breast Ultrasound Diagnosis
Wang, H., Zhang, S., Wentao, K. O. N. G., Shao, W., Zhang, D., & Wan, P. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102799
Audiogram Detection AI Trained Using Size-Fixed Frame and Bounding-Boxes
Shishido, T., Shimmura, H., Kurihara, S., Ono, Y., Kumazawa, I., Okano, H. J., & Suzuki, K. (2026) · New Generation Computing · doi:10.1007/s00354-026-00328-6
Graph-Enhanced Dynamic Interactive Cross-Modal Learning for Text-Based Person Search
Ouyang, M., Wang, C., Li, J., Yuan, X., & Yuan, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3513-1_3
Reasoning-Guided Part-Level Visual Grounding via Reinforcement Learning
Mehrab, K. S., Alomari, H., Sarker, N. H., Tang, C.-W., Hakim, Z. I. A., Karpatne, A., & Thomas, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37383-0_34
An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
Cao, M., Lu, Y., Zeng, Z., Yi, D., Wang, J., & Ye, M. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3715117
Reducing Prompt Sensitivity in Video Anomaly Understanding via Conditional Prompt Learning
Ignatosyan, A., Cao, L., & Doughty, H. (2026) · doi:10.1145/3810987.3815535
CEAP: Checkpoint Ensemble-based Active Learning and Pseudo-Labeling for annotation-efficient MLLM domain adaptation
Zhao, S., Huang, H., & Li, X. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105062
Towards vision-language-action systems: A statistical and objective study of recent advances
Bayan, M., & Bhattacharyya, R. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01215-5

Authors: Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., Li, B., Luo, P., Lu, T., Qiao, Y., & Dai, J.
Affiliations: Nanjing University (CN)
University of Hong Kong (HK)
Chinese University of Hong Kong (HK)
University of Science and Technology of China (CN)
Tsinghua University (CN)
Shanghai Artificial Intelligence Laboratory (CN)
Sensetime (China) (CN)
Nanjing University
Tsinghua University
ORCID: Chen, Zhe
Wu, Jiannan
Wang, Wenhai
Chen, Guo
Zhang, Qinglong
Lu, Lewei
Li, Bin
Venue: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2024-06-16
Pages: 24185-24198
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr52733.2024.02283
OpenAlex: W4402713111
Cited by: 734
References: 145
Citations by year: 2026: 360 · 2025: 331 · 2024: 43
FWCI: 151.51
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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)
arXiv (Cornell University)
Funders: National Natural Science Foundation of China (62376134,62372223)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: vision-language foundation models, vision foundation models, LLM alignment, zero-shot image classification, image-text retrieval, visual perception

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

Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks