KAT: A Knowledge Augmented Transformer for Vision-and-Language

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

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

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

Decoupled Weight Decay Regularization

Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821

Billion-scale similarity search with GPUs

Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228

Dense passage retrieval for open-domain question answering

Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575

Entity-Enhanced Question Representation for Knowledge-Based Visual Question Answering

Liu, H., Wang, B., Sun, Y., Li, X., Hu, Y., & Yin, B. (2024) · International Conference on Machine Vision, Image Processing and Imaging Technology (MVIPIT) · doi:10.1109/mvipit65697.2024.00025

Seek Inner: LLM-Enhanced Information Mining for Medical Visual Question Answering

Ma, A., Li, Z., Liang, Z., Gu, T., Fan, J., Long, J., Müller, H., & Cai, W. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717556

CoMuS-KG: A Collaborative Framework of Multimodal Unstructured Data and Knowledge Graph

Hu, S., Wang, X., Ji, X., Guo, X., Wang, L., & Shen, J. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033342

Efficient knowledge distillation and alignment for improved KB-VQA

Qin, X., Pei, R., He, C., Li, F., & Zhang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-07539-9

TDIUC-AVQA: A Visual Question Answering Dataset in Low-Resource Assamese Language

Rahman, N., Choudhury, P., Guha, P., Anand, A., & Nandi, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93697-5_12

Prompting Large Models for Knowledge and Reasoning Augmentation in KB-VQA

Liu, Q., Ying, M., Xiao, P., Li, G., & Yuan, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0014-7_27 · Cited by 1

Data-driven weakly supervised emotion classification with consistency regularization: Mandarin Chinese as a case

Odbal, O., Zheng, Z., & Zhang, G. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09868-7

KV-KBVQA: Effective KAN Vision Transformer for Knowledge-Based Visual Question Answering with Residual Cross-Modal Attention

He, Z., Liu, Z., Si, Y., Sun, G., Zhang, T., Tang, X., & Xu, G. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-97352-9_5

Multi-Passage Retrieval-Augmented Multimodal Language Generation Model for Knowledge-Based Visual Question Answering

Cheng, S., Yang, C., & Jiang, B. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210062

A Knowledge Noise Mitigation Framework for Knowledge-based Visual Question Answering

Liu, Z., Liu, S., Liu, J., & Zhang, X. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209827

Retrieving and Reading Multimodal Documents for Knowledge-Based VQA

Xie, W., Zhang, X., Zhang, M., & Zhang, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3349-7_7

PLMAS: Adaptive Sample Selection for Prompting LLMs in Knowledge-Based Visual Question Answering

Li, J., Xu, Q., Zhou, L., Zhang, F., & Huang, R. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3777476

Exploiting Multimodal Knowledge Graph for Multimodal Machine Translation

Xu, T., Liu, X., Wong, D. F., Zhang, Y., Chao, L. S., Zhang, M., & Gan, T. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3639995

MAMR: Modality-Aligned Multi-modal Retrieval

Xu, Y., Mo, J., Lin, F., Chen, Z., Chen, C., & Liu, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228736

A multimodal fusion and symmetric dual-encoder based generation method for KB-VQA

Zheng, H., Sun, T., Liu, H., Zhang, Z., Yang, Z., Wang, X., & Liu, G. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06992-3

SCRA-VQA: Summarized Caption-Rerank for Augmented Large Language Models in Visual Question Answering

Zhang, Y., Lin, J., Zhang, M., Xiao, K., Hou, X., Zhao, Y., & Li, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_3

MMToT: Multi-Modal Token-of-Thought Reasoning for Large Models

Xu, N., Lu, Z., Tian, H., Zheng, B., Cao, J., & Liu, A.-A. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3654463

Multi-Modal Refined Prompting for Advancing Knowledge-Based Visual Question Answering

Zhu, L., Ying, M., Zhang, C., Liu, D., Wu, L. Y., Zhang, S., & Li, X. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3660119

Focus Before Reasoning: A Bidirectional Selection Framework for Noise-Mitigation in Knowledge-Based Vision Question Answering

Zhao, H., Zhou, Z., & Ye, G. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464318

Bridging Cross-Modal Semantic Gaps with Multi-Source Semantic Anchors in Knowledge-Based Visual Question Answering

Hu, J., Zhang, J., Zhan, F., & Huang, Y. (2026) · Electronics · doi:10.3390/electronics15091837

RGS: A Unified Representation-Generation-Selection Framework for Knowledge-Based Visual Question Answering

Wang, J., Nian, X., Li, Y., Zhu, Y., Wang, H. M., Zheng, J., & Wang, Z. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3689433

IntentQA: Intent Question Answering in Videos by Cognitive Context Reasoning

Li, J. H., Wei, P., Han, W., Zhu, S. C., & Fan, L. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3690561

Relevance-aware Multi-context Contrastive Decoding for Retrieval-augmented Visual Question Answering

Kim, J., Ko, B., Na, J., Yoon, J., & Kim, H. J. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00829

Semantic Quality Control of EMR Admission Notes: Integrating Rule Guidance, Prompt Optimization, and RAG

Liu, Y., Pan, Y., Zhen, W., & Duan, J. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7299-1_25

Ibcl: instance-aware bias calibration with contrastive learning for visual question answering

Liu, C., Wu, H., Sun, B., & Fang, Z. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02402-x

Knowledge Generation for Knowledge-Based Visual Question Answering

Liu, J., Zhang, L. Y., Zhao, Z., Tiliwalidi, K., Li, Y., & Chenpeng, C. (2025) · doi:10.1109/ccat68690.2025.00035

VC-MARAG: Visual-Context Augmented Retrieval and Multi-agent Collaborative Generation for VQA

Chen, Y., Chen, J., Liu, G., Li, D., Cai, S., & Ming, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2856-0_43

Plan-and-Refine in RAG: Generating Diverse and Comprehensive Responses through Global Exploration and Local Exploitation

Salemi, A., Samarinas, C., & Zamani, H. (2026) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3805713.3820415 · Cited by 1

CB-VQA: Chain-Bridge Visual Question Answering via Question-Aware Intermediate Validation

Cui, M., Ge, H., Yan, J. M., Liu, Y., & Long, S. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3723109

A visual question answering model based on entity knowledge selection

Zhu, K., Zhou, K., & Zhao, D. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02627-w

Beyond modality importance: Reliability-guided tree memory for inductive multimodal knowledge graph completion

Wang, J., Gao, J., Li, W., Zhang, X., & Wang, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116953

Fine-Grained Visual Prompt and Region Self-Distillation for Retrieval-Augmented Vqa

Wang, Y., Zhang, H., Liang, J., Wang, Z., Tan, H., & Li, R. (2026) · doi:10.1109/cvprf71909.2026.00890

Bridging the Selectivity Gap in Zero-Shot Visual Question Answering via Intrinsic Answer Verification

Tang, X., Ma, L., & Luo, X. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728462

Imagination-Augmented Natural Language Understanding

Lu, Y., Zhu, W., Wang, X. E., Eckstein, M. P., & Wang, W. Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.326 · Cited by 21

KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models

Jia, Z., Narayana, P., Akula, A., Pruthi, G., Su, H., Basu, S., & Jampani, V. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.74 · Cited by 8

ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models

Zhang, J., Muhamed, A., Anantharaman, A., Wang, G., Chen, C., Zhong, K., Cui, Q., Xu, Y., Zeng, B., Chilimbi, T. M., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.97 · Cited by 17

QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models

Zhang, Y., Xie, R., Chen, J., Sun, X., Kang, Z., & Wang, Y. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.512 · Cited by 1

Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models

Ai, Q., Li, J., Dai, J., Zhou, J., Liu, L., Jiang, H., & Shi, S. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.404 · Cited by 2