Hallusionbench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Open accessAbstract
We introduce “HALLUSIONBENCH11“Hallusion” is a portmanteau of “hallucination” and “illusion.”,” a comprehensive benchmark designed for the evaluation of image-context rea-soning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs), such as GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, by emphasizing nuanced understanding and interpre-tation of visual data. The benchmark comprises 346 images paired with 1129 questions, all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies, logical consistency, and various failure modes. In our evaluation on Hallusion-bench, we benchmarked 15 different models, highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably, all other evaluated models achieve accuracy below 16%. Moreover, our analysis not only high-lights the observed failure modes, including language hal-lucination and visual illusion but also deepens an under-standing of these pitfalls. Our comprehensive case studies within Hallusionbench shed light on the challenges of hallucination and illusion in LVLMs. Based on these in-sights, we suggest potential pathways for their future im-provement. The benchmark and codebase can be accessed at https://github.com/tianyi-labIHallusionBench.
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
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?
One shortcoming of frontier multimodal models is that they may not perceive the image in detail, often missing fine-grained details or ignoring the image entirely.
“One such shortcoming is that models may not perceive the image in detail, often missing fine-grained details or ignoring the image entirely (Chen et al., 2024b; Guan et al., 2024; Tong et al., 2024).”
From Does Spatial Cognition Emerge in Frontier Models? (2024) · cites this paper as Guan et al., 2024
Current LVLMs are suffering from multi-modal hallucinations, where models provide responses misaligned with the corresponding visual information.
“Recent studies have revealed that current LVLMs are suffering from multi-modal hallucinations, where models provide responses misaligned with the corresponding visual information [104, 187, 297].”
From A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2025) · cites this paper as [104]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningPreprint
Dai, W., Li, J., Li, D., Tiong, A., Zhao, J.-Q., Wang, W., Li, B. A., Fung, P., & Hoi, S. (2023) · Neural Information Processing Systems · doi:10.48550/arxiv.2305.06500 · Cited by 4,191 · see citing works
Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1516 · Cited by 2,428 · see citing works
Proceedings of IEEE International Conference on Computer Vision
(1995) · doi:10.1109/iccv.1995 · Cited by 2,339 · see citing works
A Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Dong, Z., Hou, Y., Zhang, B., Min, Y., Zhang, J., Liu, P., et al. (2026) · Frontiers of Computer Science · doi:10.1007/s11704-026-60308-3 · Cited by 2,093 · cited 1× · see citing works
“In recent years, Large Language Models (LLMs) [9, 10, 26, 40, 45, 46, 61] have revolutionized the field of machine learning with the ability of language understanding and content generation, offering unprecedented ca-* pabilities and potentials across a multitude of applications.”
BackgroundSelf-citationA Survey of Large Language Models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090
Self-citationImproved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748 · see citing works
Flamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555 · see citing works
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
mPLUG-Owl: Modularization Empowers Large Language Models with MultimodalityPreprint
Ye, Q.-H., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P.-C., Shi, Y., et al. (2023) · arXiv.org · doi:10.48550/arxiv.2304.14178 · Cited by 1,318 · see citing works
Self-citationThe Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)Preprint
Yang, Z.-Y., Li, L., Lin, K., Wang, J., Lin, C.-C., Liu, Z., & Wang, L.-J. (2023) · arXiv.org · doi:10.48550/arxiv.2309.17421 · Cited by 1,036 · see citing works
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.11805 · Cited by 990 · see citing works
Li, J., Li, D., Savarese, S., & Hoi, S. (2023) · arXiv · doi:10.48550/arxiv.2301.12597 · Cited by 908 · see citing works
A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., & Chen, E. (2024) · National Science Review · doi:10.1093/nsr/nwae403 · Cited by 893 · see citing works
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879 · cited 1× · see citing works
“These models have demonstrated profound capabilities in various applications and significantly enhance the performance in image reasoning tasks [5, 18, 20, 30, 31, 36, 38, 42, 47].”
BackgroundShow and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2587640 · Cited by 875 · see citing works
GIT: A Generative Image-to-text Transformer for Vision and LanguagePreprint
Wang, J., Yang, Z.-Y., Hu, X., Li, L., Lin, K., Gan, Z., Liu, Z.-C., Liu, C., & Wang, L.-J. (2022) · Trans. Mach. Learn. Res. · doi:10.48550/arxiv.2205.14100 · Cited by 817 · see citing works
mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
Ye, Q.-H., Xu, H., Ye, J., Yan, M.-S., Hu, A., Liu, H.-W., Qian, Q., Zhang, J., Huang, F., & Zhou, J.-R. (2023) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.01239 · Cited by 763 · see citing works
Self-citationLiu, H., Li, C., Wu, Q., & Lee, Y. J. (2023) · arXiv · doi:10.48550/arxiv.2304.08485 · Cited by 682 · see citing works
MMBench: Is Your Multi-modal Model an All-Around Player?
Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72658-3_13 · Cited by 668 · see citing works
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and ActionPreprint
Yang, Z.-Y., Li, L., Wang, J., Lin, K., Azarnasab, E., Ahmed, F., Liu, Z.-C., Liu, C., Zeng, M., & Wang, L.-J. (2023) · arXiv.org · doi:10.48550/arxiv.2303.11381 · Cited by 660 · see citing works
PaLM-E: An Embodied Multimodal Language Model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · see citing works
Evaluating Object Hallucination in Large Vision-Language Models
Li, Y., Du, Y., Zhou, K., Wang, J., Xin, Z., & Wen, J.-R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.20 · Cited by 580 · see citing works
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., et al. (2025) · Computational Linguistics · doi:10.1162/coli.a.16 · Cited by 534 · see citing works
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Masry, A., Long, X., Tan, J. Q., Joty, S., & Hoque, E. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.177 · Cited by 508 · see citing works
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., & Parikh, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1116-0 · Cited by 487 · see citing works
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Zhu, D., Chen, J., Shen, X., Li, X., & Elhoseiny, M. (2023) · arXiv · doi:10.48550/arxiv.2304.10592 · Cited by 469 · see citing works
nocaps: novel object captioning at scale
Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., & Anderson, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00904 · Cited by 386 · see citing works
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image UnderstandingPreprint
Zhang, Y., Zhang, R., Gu, J., Zhou, Y., Lipka, N., Yang, D.-Y., & Sun, T.-F. (2023) · arXiv.org · doi:10.48550/arxiv.2306.17107 · Cited by 335 · see citing works
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.00598 · Cited by 205 · see citing works
Woodpecker: hallucination correction for multimodal large language models
Yin, S., Fu, C., Zhao, S., Xu, T. B., Wang, H., Sui, D., Shen, Y., Li, K., Sun, X., & Chen, E. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4251-x · Cited by 196 · see citing works
Peng, B., Li, C., He, P., Galley, M., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2304.03277 · Cited by 183 · see citing works
Detecting and Preventing Hallucinations in Large Vision Language Models
Gunjal, A., Yin, J., & Bas, E. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29771 · Cited by 161 · see citing works
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Xu, L., Yang, J., Zheng, X., Li, K., Sun, X. W., et al. (2025) · neural information processing systems · doi:10.52202/085713-4899 · Cited by 148 · see citing works
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference ChallengesPreprint
Cui, C.-H., Zhou, Y.-Y., Yang, X.-Y., Wu, S., Zhang, L.-J., Zou, J., & Yao, H. (2023) · arXiv.org · doi:10.48550/arxiv.2311.03287 · Cited by 145 · see citing works
Larger language models do in-context learning differently
Wei, J., Wei, J., Tay, Y., Tran, D., Webson, A., Lu, Y., Chen, X., Liu, H., Huang, D., Zhou, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03846 · Cited by 109 · see citing works
Transform and Tell: Entity-Aware News Image Captioning
Tran, A., Mathews, A., & Xie, L. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01305 · Cited by 91 · see citing works
Visual News: Benchmark and Challenges in News Image Captioning
Liu, F., Wang, Y., Wang, T., & Ordóñez, V. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.542 · Cited by 78 · see citing works
Self-citationMM-Vet: Evaluating Large Multimodal Models for Integrated CapabilitiesPreprint
Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., & Wang, L. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.02490 · Cited by 74 · see citing works
Self-citationScienceQA: a novel resource for question answering on scholarly articles
Saikh, T., Ghosal, T., Mittal, A., Ekbal, A., & Bhattacharyya, P. (2022) · International Journal on Digital Libraries · doi:10.1007/s00799-022-00329-y · Cited by 72 · see citing works
MultiModal-GPT: a vision and language model for dialogue with humans
Gong, T., Lyu, C., Zhang, S., Wang, Y., Zheng, M., Zhao, Q., Liu, K., Zhang, W., Luo, P., & Chen, K. (2023) · arXiv · doi:10.48550/arxiv.2305.04790 · Cited by 70 · see citing works
LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark
Yin, Z., Wang, J., Cao, J., Shi, Z., Liu, D., Li, M., Huang, X., Wang, Z., Sheng, L., Bai, L., et al. (2023) · neural information processing systems · doi:10.52202/075280-1158 · Cited by 59 · see citing works
VIGC: Visual Instruction Generation and Correction
Wang, B., Wu, F., Han, X. H., Peng, J., Zhong, H., Zhang, P., Dong, X., Li, W., Li, W., Wang, J., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i6.28338 · Cited by 42 · see citing works
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue DataPreprint
Li, Y., Zhang, C., Yu, G., Wang, Z.-B., Fu, B., Lin, G., Shen, C.-H., Chen, L., & Wei, Y.-C. (2023) · arXiv.org · doi:10.48550/arxiv.2308.10253 · Cited by 37 · see citing works
Self-citationWhat Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
Zeng, Y., Zhang, H., Zheng, J., Xia, J., Wei, G., Wei, Y., Zhang, Y., Kong, T., & Song, R. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.440 · Cited by 35 · see citing works
COVID-VTS: Fact Extraction and Verification on Short Video Platforms
Liu, F., Yacoob, Y., & Shrivastava, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.14 · Cited by 28 · see citing works
Self-citationPrivacyMind: Large Language Models Can Be Contextual Privacy Protection LearnersPreprint
Xiao, Y., Jin, Y., Bai, Y., Wu, Y., Yang, X., Luo, X., Yu, W.-C., Zhao, X.-J., Liu, Y.-C., Gu, Q.-Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.02469 · Cited by 25 · see citing works
AlpaGasus: Training A Better Alpaca with Fewer DataPreprint
Chen, L., Li, S., Yan, J., Wang, H., Gunaratna, K., Yadav, V., Tang, Z., Srinivasan, V., Zhou, T., Huang, H., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.08701 · Cited by 22 · see citing works
Self-citationSciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific GraphsPreprint
Li, S., & Tajbakhsh, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.03349 · Cited by 19 · see citing works
MiniGPT-5: Interleaved Vision-and-Language Generation via Generative Vokens
Zheng, K., He, X., & Wang, X. E. (2023) · arXiv · doi:10.48550/arxiv.2310.02239 · Cited by 15 · see citing works
Self-citationGrounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans?
Zhang, Y., Pan, J., Zhou, Y., Pan, R., & Chai, J. Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.348 · Cited by 12 · see citing works
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., & Gao, J. (2023) · arXiv · doi:10.48550/arxiv.2310.02255 · Cited by 12 · see citing works
DocumentCLIP: Linking Figures and Main Body Text in Reflowed DocumentsPreprint
Liu, F., Tan, H., & Tensmeyer, C. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.06306 · Cited by 5 · see citing works
Self-citationModule-wise Adaptive Distillation for Multimodality Foundation Models
Liang, C., Yu, J., Yang, M.-H., Brown, M., Cui, Y., Zhao, T., Gong, B., & Zhou, T. (2023) · neural information processing systems · doi:10.52202/075280-3055 · Cited by 5 · see citing works
Self-citationLOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and NavigationPreprint
Guan, T., Yang, Y., Cheng, H. H., Lin, M., Kim, R., Madhivanan, R., Sen, A., & Manocha, D. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.05363 · Cited by 3 · see citing works
Self-citationStrohm, C. (2015) · Religion in Geschichte und Gegenwart · doi:10.1163/2405-8262_rgg4_sim_03081 · Cited by 1 · see citing works
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Li, B., Wang, R., Wang, G., Ge, Y., Ge, Y., & Shan, Y. (2023) · arXiv · doi:10.48550/arxiv.2307.16125
Flagged first, then by citations
7 of the 65 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. ArXiv, abs/2308.12966, 2023. 6, 7 · arXiv
- Zongxia Li, Paiheng Xu, Fuxiao Liu, and Hyemi Song. To- wards understanding in-context learning with contrastive demonstrations and saliency maps. arXiv preprint arXiv:2307.05052, 2023. · arXiv
- Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. Aligning large multi-modal model with robust instruction tuning. arXiv preprint arXiv:2306.14565, 2023. 1, 2, 3, 4, 6, 7 · arXiv
- Yuexiang Zhai, Shengbang Tong, Xiao Li, Mu Cai, Qing Qu, Yong Jae Lee, and Yi Ma. Investigating the catastrophic for- getting in multimodal large language models. arXiv preprint arXiv:2309.10313, 2023. 1 · arXiv
Cited by
Sorted by citations
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3703155 · Cited by 2,142 · see citing works
BLINK: Multimodal Large Language Models Can See but Not Perceive
Fu, X., Hu, Y., Li, B., Feng, Y., Wang, H., Lin, X., Roth, D., Smith, N. A., Ma, W.-C., & Krishna, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73337-6_9 · Cited by 101 · see citing works
Li, Z., Wu, X., Du, H., Liu, F., Nghiem, H. T., & Shi, G. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00147 · Cited by 72 · see citing works
Self-citationLlaVA-CoT: Let Vision Language Models Reason Step-By-Step
Xu, G., Jin, P., Wu, Z., Li, H., Song, Y., Sun, L., & Yuan, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00202 · Cited by 118 · see citing works
Cirone, K. D., Akrout, M., Abid, L., & Oakley, A. M. (2024) · JMIR Dermatology · doi:10.2196/55508 · Cited by 43 · see citing works
An Empirical Evaluation of the GPT-4 Multimodal Language Model on Visualization Literacy Tasks
Bendeck, A., & Stasko, J. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456155 · Cited by 40 · see citing works
Putting ChatGPT vision (GPT-4V) to the test: risk perception in traffic images
Driessen, T., Dodou, D., Bazilinskyy, P., & de Winter, J. (2024) · Royal Society Open Science · doi:10.1098/rsos.231676 · Cited by 36 · see citing works
Danish, S., Sadeghi‐Niaraki, A., Khan, S. U., Dang, L. M., Tightiz, L., & Moon, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103623 · Cited by 38 · see citing works
Wu, D., Zheng, P., Zhao, Q., Zhang, S., Qi, J., Hu, J., Zhu, G., & Wang, L. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103064 · Cited by 35 · see citing works
Shi, Y., Gao, Y., Lai, Y., Wang, H., Feng, J. Z., He, L., Wan, J., Chen, C., Yu, Z., & Cao, X. (2025) · Visual Intelligence · doi:10.1007/s44267-025-00079-w · Cited by 37 · see citing works
Benchmark Evaluations, Applications, and Challenges of Large Vision Language Models: A SurveyPreprint
Li, Z., Wu, X., Du, H., Nghiem, H. T., & Shi, G. (2025) · Qeios · doi:10.32388/gxr68q · Cited by 30 · see citing works
Self-citationPathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology
Sun, Y., Wu, H., Zhu, C., Zheng, S., Chen, Q., Zhang, K., Zhang, Y., Wan, D., Lan, X., Zheng, M., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_4 · Cited by 24 · see citing works
Jiang, C., Jia, H., Dong, M., Ye, W., Xu, H., Yan, M., Zhang, J., & Zhang, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680576 · Cited by 20 · see citing works
Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2511.21631 · Cited by 27 · see citing works
Self-citationViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality
Xiu, Y., Scargill, T., & Gorlatova, M. A. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3549147 · Cited by 16 · see citing works
GPT4Ego: Unleashing the Potential of Pre-Trained Models for Zero-Shot Egocentric Action Recognition
Dai, G., Shu, X., Wu, W., Yan, R., & Zhang, J. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521658 · Cited by 14 · see citing works
Luo, G., Yang, X., Dou, W., Wang, Z., Liu, J., Dai, J., Qiao, Y., & Zhu, X. (2025) · 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/cvpr52734.2025.02324 · Cited by 23 · see citing works
Li, G., Xu, J., Zhao, Y., & Peng, Y. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.00850 · Cited by 12 · see citing works
Imp: Highly Capable Large Multimodal Models for Mobile Devices
Shao, Z., Zhou, Y., Yu, J., Ouyang, X., Zheng, L., Gai, Z., Wang, M., Kuang, Z., & Ding, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3557680 · Cited by 15 · see citing works
BLIP-3: A Family of Open Large Multimodal Models
Xue, L., Shu, M., Awadalla, A., Wang, J., Yan, A., Purushwalkam, S., Zhou, H., Prabhu, V., Dai, Y.-T., Ryoo, M. S., et al. (2024) · 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw69036.2025.00644 · Cited by 129 · see citing works
Anderer, K., Müller, K., Strobel, L., Wölfel, M., Niehues, J., & Gerling, K. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746349 · Cited by 10 · see citing works
PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
Liu, J., Fu, Y., Xie, R., Xie, R., Sun, X., Lian, F., Kang, Z., & Li, X. (2025) · 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/cvpr52734.2025.01849 · Cited by 10 · see citing works
Attention Reallocation: Towards Zero-cost and Controllable Hallucination Mitigation of MLLMs
Tu, C., Ye, P. D., Zhou, D., Bai, L., Yu, G., Chen, T., & Ouyang, W. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02607-z · Cited by 9 · see citing works
Hu, L., Wang, D., Pan, Y., Yu, J., Shao, Y., Feng, C., & Nie, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680790 · Cited by 9 · see citing works
Dang, R., Yuan, Y. F., Zhang, W., Xin, Y., Zhang, B., Li, L., Wang, L., Zeng, Q., Li, X., & Bing, L. (2025) · 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/cvpr52734.2025.02290 · Cited by 8 · see citing works
Diagram Formalization Enhanced Multi-Modal Geometry Problem Solver
Zhang, Z., Cheng, J.-K., Deng, J., Tian, L., Ma, J., Qin, Z., Zhang, X., Zhu, N., & Leng, T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889286 · Cited by 7 · see citing works
UniToken: Harmonizing Multimodal Understanding and Generation Through Unified Visual Encoding
Jiao, Y., Qiu, H.-B., Jie, Z.-Q., Chen, S.-X., Chen, J.-J., Ma, L., & Jiang, Y.-G. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00345 · Cited by 56 · see citing works
Jiang, F., Ma, J., & Jin, Y. (2025) · Smart Cities · doi:10.3390/smartcities8060201 · Cited by 7 · see citing works
A Survey of Multimodal Hallucination Evaluation and Detection
Chen, Z., Min, Y., Zhang, J., Yan, B., Wang, J., Wang, X., & Shan, S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-026-02756-9 · Cited by 11 · see citing works
Self-citationEvaluating LLMs for visualization generation and understanding
Khan, S. R., Chandak, V., & Mukherjea, S. (2025) · Discover Data · doi:10.1007/s44248-025-00036-4 · Cited by 6 · see citing works
Visual hallucination detection in large vision-language models via evidential conflict
Huang, T., Liu, Z., Wang, R., Zhang, Y., & Jing, L. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109507 · Cited by 6 · see citing works
BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices
Lü, X., Chen, Y., Chen, C., Tan, H. S., Chen, B., Xie, Y., Hu, R., Tan, G., Wu, R., Hu, Y., et al. (2025) · 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/cvpr52734.2025.00392 · Cited by 12 · see citing works
Abdullahi, S., Danyaro, K. U., & Chiroma, H. (2026) · Cluster Computing · doi:10.1007/s10586-025-05891-z · Cited by 7 · see citing works
ChartReformer: Natural Language-Driven Chart Image Editing
Yan, P., Bhosale, M., Lal, J., Adhikari, B., & Doermann, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70533-5_26 · Cited by 5 · see citing works
Li, C., Xing, B., Huo, D., Zhou, Q., Xu, Z., & Wang, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889328 · Cited by 5 · see citing works
Improving Multimodal Large Language Models through Combining Resampler and MLP Projections
Bai, Z., & Bai, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888340 · Cited by 5 · see citing works
HumanMoD: A multi-RAG collaborative LLM for inclusive urban public healthcare services
Sun, S. M., Zhong, Z., Yu, N., Gong, X., & Yang, K. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113684 · Cited by 4 · see citing works
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Wang, W., Chen, Z., Liu, Y., Cao, Y., Wang, W., Zhu, X., Lu, L., Lü, T., Qiao, Y., & Dai, Y. (2025) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-031-94969-2_2 · Cited by 4 · see citing works
MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
Li, J., Wu, M., Jin, Z., Chen, H., Ji, J., Sun, X., Cao, L., & Ji, R. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754993 · Cited by 5 · see citing works
Jiang, S., Wang, Z., Wu, X., Ji, H., Li, M. X., Li, X., & Gao, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754817 · Cited by 5 · see citing works
Self-citationMultiple Large AI Models’ Consensus for Object Detection—A Survey
Iwanowski, M., & Gahbler, M. (2025) · Applied Sciences · doi:10.3390/app152412961 · Cited by 6 · see citing works
Mitigating Object Hallucinations via Sentence-Level Early Intervention
Peng, S., Yang, S., Jiang, L., & Tian, Z.-T. (2025) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51701.2025.00067 · Cited by 32 · see citing works
Progress-Aware Video Frame Captioning
Xue, Z., An, J., Yang, X., & Grauman, K. L. (2025) · 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/cvpr52734.2025.01273 · Cited by 4 · see citing works
Benchmarking large and small MLLMs
Feng, X.-L., Li, Y.-S., Chen, D.-D., Gao, M., Liu, M.-C., Yuan, J.-S., & Qiao, C.-M. (2025) · Machine Vision and Applications · doi:10.1007/s00138-025-01762-0 · Cited by 7 · see citing works
Zhang, Y., Zhang, Z., Wei, X., Liu, X., Zhai, G., & Min, X. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11208962 · Cited by 6 · see citing works
Self-citationZSORN: Language-Driven Object-Centric Zero-Shot Object Retrieval and Navigation
Guan, T., Yang, Y., Cheng, H. H., Lin, M., Kim, R., Madhivanan, R., Sen, A., & Manocha, D. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128436 · Cited by 4 · see citing works
Self-citationLBAP: Improved Uncertainty Alignment of LLM Planners using Bayesian Inference
Mullen, J. F., & Manocha, D. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11245905 · Cited by 3 · see citing works
Self-citationChain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens
Qin, Y., Wei, B., Ge, J., Kallidromitis, K., Fu, S., Darrell, T., & Wang, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37359-5_25 · Cited by 3 · see citing works
Self-citationThe Art of Deception: Color Visual Illusions and Diffusion Models
Gomez-Villa, A., Wang, K., Párraga, C. A., Twardowski, B., Malo, J., Vázquez-Corral, J., & van de Weijer, J. (2025) · 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/cvpr52734.2025.01737 · Cited by 3 · see citing works
Yan, H., Chang, Z., Tang, B., Lin, B., Luo, M., Bi, Y., & Li, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128392 · Cited by 3 · see citing works
Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-training
Wang, H., Ju, C., Lin, W., Xiao, S., Chen, M., Huang, Y., Liu, C., Yao, M., Lan, J., Chen, Y., et al. (2025) · 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/cvpr52734.2025.02773 · Cited by 3 · see citing works
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games
Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.13543 · Cited by 2 · see citing works
“We provide Crafter results for LLM and VLM format in Tables 6 and 7, standard errors are computed using 10 seeds.”
InfluentialSun, Y., Wu, H., Zhu, C., Si, Y., Chen, Q., Zhang, Y., Zhang, K., Li, J., Cai, J., Wang, Y., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3584857 · Cited by 2 · see citing works
Spatial Knowledge Graph-Guided Multimodal Synthesis
Xue, Y., Bi, Z., Yang, J., Lou, J., Chen, K., Zhang, M., Chen, H., & Zhang, N. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3633069 · Cited by 2 · see citing works
Enhancing OCR‐based Engineering Diagram Analysis by Integrating Diverse External Legends with VLMs
Shteriyanov, V., Джусупова, Р., Bosch, J., & Olsson, H. H. (2025) · Journal of Software Evolution and Process · doi:10.1002/smr.70072 · Cited by 2 · see citing works
Boosting Faithful Multi-Modal LLMs via Complementary Visual Grounding
Fu, Z., Mao, Z.-D., Zhang, L., & Zhang, Y.-D. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3644140 · Cited by 6 · see citing works
A Cognitive Architecture for Embodied AI Based on LLM Common-Sense Knowledge
Saladino, A., Brienza, M., Suriani, V., Bloisi, D. D., & Iocchi, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2398-6_23 · Cited by 2 · see citing works
Mitigating Hallucination in Visual-Language Models via Re-balancing Contrastive Decoding
Liang, X., Yu, J., Mu, L., Zhuang, J., Hu, J., Yang, Y.-C. B., Ye, J., Lu, L., Chen, J., & Hu, H. R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8620-6_33 · Cited by 2 · see citing works
MedicalGLM: A Pediatric Medical Question Answering Model with a quality evaluation mechanism
Wang, X., Sun, Z., Wang, P., & Wei, B. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104793 · Cited by 2 · see citing works
Self-citationBenchmarking Vision Language Models on German Factual Data
Peinl, R., & Tischler, V. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96235-6_20 · Cited by 2 · see citing works
VisionArena: 230K Real World User-VLM Conversations with Preference Labels
Chou, C., Dunlap, L., Mashita, K., Mandal, K., Darrell, T., Stoica, I. G., Gonzalez, J. E., & Chiang, W.-L. (2025) · 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/cvpr52734.2025.00367 · Cited by 3 · see citing works
A survey of visual insight mining: Connecting data and insights via visualization
Lian, Y., Hao, J., Zeng, W., & Luo, Q. (2025) · Visual Informatics · doi:10.1016/j.visinf.2025.100271 · Cited by 2 · see citing works
Xie, J., Gao, Y., You, L. H., Xu, X. H., Xu, H., Kou, Z., Fu, K., Qu, Y., Yang, W., Guo, J., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755320 · Cited by 2 · see citing works
On the Vulnerability of LLM/VLM-Controlled Robotics
Wu, X., Chakraborty, S., Xian, R., Liang, J., Guan, T., Liu, F., Sadler, B. M., Manocha, D., & Bedi, A. S. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246863 · Cited by 8 · see citing works
Self-citationVideoHallu: Evaluating and Mitigating Multi-modal Hallucinations for Synthetic VideosPreprint
Li, Z., Wu, X., Qin, Y., Shi, G., Du, H., Manocha, D., Zhou, T., & Boyd-Graber, J. L. (2025) · Qeios · doi:10.32388/bxc6x1 · Cited by 2 · see citing works
Self-citationA Survey on Hallucination in Large Language and Foundation ModelsPreprint
Ahadian, P., & Guan, Q. (2025) · Preprints.org · doi:10.20944/preprints202504.1236.v2 · Cited by 3 · see citing works
V-PRUNE: Semantic-Aware Patch Pruning Before Tokenization in Vision–Language Model Inference
Seo, H., & Choi, Y. S. (2025) · Applied Sciences · doi:10.3390/app15179463 · Cited by 1 · see citing works
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
Rostamkhani, M., Ansari, B., Sabzevari, H., Rahmani, F., & Eetemadi, S. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00282 · Cited by 1 · see citing works
Kim, Y., & Jung, J. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00062 · Cited by 1 · see citing works
Multimodal LLM using Federated Visual Instruction Tuning for Visually Impaired
Bala, A., & Vereshchaka, A. (2025) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3716553.3750763 · Cited by 1 · see citing works
Wang, Z., Li, L., Chen, L., Xu, X., Li, M., & Su, Y. (2025) · International Conference on Wireless Communication, Networking and Internet of Things (WCNIoT) · doi:10.1109/wcniot67424.2025.11381315 · Cited by 1 · see citing works
Self-citationHow to Train Your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions
Brack, M., Katakol, S., Friedrich, F., Schramowski, P., Ravi, H., Kersting, K., & Kale, A. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00711 · Cited by 2 · see citing works
Li, Y., Wang, Y., Li, M., Li, X., & Feng, J. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115962 · Cited by 1 · see citing works
SMART: When is It Actually Worth Expanding a Speculative Tree?
Wang, L., & Zhou, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37627-5_13 · Cited by 1 · see citing works
Fuzzy Contrastive Decoding to Alleviate Object Hallucination in Large Vision-Language Models
Kim, J., Kim, J., Kim, Y., & 조성배 (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01913 · Cited by 1 · see citing works
Lu, X., Chen, Y., Wu, R., Gao, H., Chen, X., Yang, X., Zhao, X., Zhou, A., Li, F., Wen, Y., et al. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00400 · Cited by 1 · see citing works
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
Wang, X., Yang, Z., Li, L., Lu, H., Xu, Y., Lin, C.-C., Lin, K., Huang, F., & Wang, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00117 · Cited by 1 · see citing works
Self-citationVLRMBench: A Comprehensive and Challenging Benchmark for Vision-Language Reward Models
Ruan, J., Yuan, W., Gao, X., Guo, Y., Zhang, D., Xu, Z., Hu, Y., Liu, T., & Fu, Y. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00303 · Cited by 1 · see citing works
Ma, N., Lin, S., Dong, X., & Ji, H. (2026) · Behavioral Sciences · doi:10.3390/bs16050732 · Cited by 1 · see citing works
Reading With, Not Through, AI: A Human-Centered Framework for Multimodal Literature Education
Mašát, M. (2026) · Review of Artificial Intelligence in Education · doi:10.37497/rev.artif.intell.educ.v7ii.1377 · Cited by 1 · see citing works
InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression
Lu, D., Zhang, Z., Huang, L., Sun, Y., Zeng, J., Shu, M., & Cao, H. (2026) · doi:10.1109/cvprf71909.2026.00554 · Cited by 2 · see citing works
MESH - Understanding Videos Like Human: Measuring Hallucinations in Large Video Models
Yang, G., Chen, Z., Wong, M. H., Lei, H., Chen, Y., Li, Z., Zhou, K., & Cheng, J. S.-C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755626 · Cited by 1 · see citing works
Self-citationSpatial Referencing for Large Language Models in Automotive Navigation Tasks
Huynh, K., Dillmann, J., & Mayer, S. (2025) · International Conference on Mobile and Ubiquitous Multimedia (MUM) · doi:10.1145/3771882.3771917 · Cited by 1 · see citing works
A Survey on Hallucination in Large Language and Foundation ModelsPreprint
Ahadian, P., & Guan, Q. (2025) · Preprints.org · doi:10.20944/preprints202504.1236.v1 · Cited by 2 · see citing works
PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
Lin, Z., Xie, Y., Qu, B., Wang, H., Li, J., Wu, H., Dong, Y., Yang, Z., Zhu, J., Lu, H., et al. (2026) · arXiv · doi:10.48550/arxiv.2607.24957
Self-citationStop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Arviv, O., Greenewald, K., Perlitz, Y., Mulian, H., Shmueli-Scheuer, M., & Choshen, L. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.43
Does Spatial Cognition Emerge in Frontier Models?
Ramakrishnan, S. K., Wijmans, E., Kraehenbuehl, P., & Koltun, V. (2024) · arXiv · doi:10.48550/arxiv.2410.06468
Tracking Technologies in Virtual RealityPreprint
Li, W., Feng, J. J., & Wang, J. (2024) · doi:10.36227/techrxiv.171018324.42048057/v1
Overview of the NLPCC 2024 Shared Task 10: Regulating Large Language Models
Wang, C., Xu, Z., Wang, M., Chen, X., Deng, S., & Zhang, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9443-0_22
Scalable Tabular Dataset Labeling with Language Models
Hu, Y., Fountalis, I., Tian, J. W., & Vasiloglou, N. (2025) · International Conference on Scalable Scientific Data Management (SSDBM) · doi:10.1145/3733723.3733739
WildVideo: Benchmarking LMMs for Understanding Video-Language Interaction
Yang, S., Yu, W., Yang, W., Liu, X., Tan, H., Lan, L., & Xiao, N. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3592831
Self-citationEMMU: Efficient Information-Level Multimodal Machine Unlearning with High Model Fidelity
Zhang, J., Hou, J., Xiao, T., Huang, Y., & Li, X.-Y. (2025) · International Workshop on Quality of Service · doi:10.1109/iwqos65803.2025.11143267
BOLT: Fewer Tokens but More Performance Retention for Efficient Vision-Language Models Inference
Bao, J., Cheng, S., Du, J., He, C., Lang, Z., Zhang, H., & Liu, J. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755305
Towards High Robust Vision-Language Large Models: Benchmark and Method
Zhao, M., Liu, Y., He, W., Yu, B., Mi, Y., & Zhou, S. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3758234
NEXUS-O: An Omni-Perceptive and -Interactive Model for Language, Audio, and Vision
Liu, C., Zhang, Y., Zhang, D., Zhang, W., Gong, C., Lu, Y., Zhou, S., Gan, Z., Wang, Z., Wu, H., et al. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754752
Bao, J., Cheng, S., Du, J., Li, Z., He, C., & Liu, J. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209794
Self-citationHow Vision-Language Tasks Benefit From Large Pre-Trained Models: A Survey
Qi, Y., Hong-xi, L., Song, Y., Wu, X., & Luo, J. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3632653 · Cited by 4 · see citing works
Self-citationBoosting Multi-Modal Large Language Model With Enhanced Visual Features
Ma, Y., Lin, W., Wang, Z., Ji, J., Sun, X., Lin, C., & Ji, R. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3644851
Multimodal Sentiment Analysis via Spatio-Temporal Decoupling and Language-Focused Fusion
Li, J., Zhang, L., Cui, H., Wang, J., & Li, R. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5567-3_17
Sentiment Intensity Contrastive Text‐Enhanced Fusion Network
Jiang, H., Shi, L., Kong, D., Hua, J., Shang, H., & Chen, L. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70593
Self-citation
- Authors
- Guan, T., Liu, F., Wu, X., Xian, R., Li, Z., Liu, X., Wang, X., Chen, L., Huang, F., Yacoob, Y., Manocha, D., & Zhou, T.
- Affiliations
- University of Maryland, College Park (US)
- ORCID
- Venue
- 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2024-06-16
- Pages
- 14375-14385
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4402726948
- Cited by
- 166
- References
- 65
- Citations by year
- 2026: 77 · 2025: 76 · 2024: 13
- FWCI
- 34.38
- 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)
- arXiv (Cornell University)
- Funders
- ARO (W911NF2310046,W911NF2310352)
- NSF (IIS-2147276 FAI)
- DOD (N00014-22-1-2335,FA9550-23-1-0048)
- 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 › Natural Language Processing Techniques
- Keywords
- hallucinations, optical illusions, large vision-language models, benchmark, failure modes, GPT-4V, LLaVA-1.5, control groups, visual question answering
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
