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

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) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335

ViperGPT: Visual Inference via Python Execution for Reasoning

Surís, D., Menon, S., & Vondrick, C. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01092 · Cited by 273

Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering

Shao, Z., Zhou, Y., Wang, M., & Yu, J. (2023) · 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/cvpr52729.2023.01438 · Cited by 240

Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends

Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000105 · Cited by 155

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

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models

Liu, J., Xing, B., Yuan, C., ChenxiangZhang, C., Wu, D., Huang, X., Yu, H., Lang, C., Cao, P., Zhao, J. F., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-demo.20

LAVIS: A One-stop Library for Language-Vision Intelligence

Li, D., Li, J., Le, H., Wang, G., Savarese, S., & Hoi, S. C. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.3 · Cited by 68

An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models

Zhao, T., Zhang, T., Zhu, M., Shen, H., Lee, K., Lu, X., & Yin, J. (2022) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2022.emnlp-demos.4 · Cited by 34

MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models

Monajatipoor, M., Li, L. H., Rouhsedaghat, M., Yang, L. F., & Chang, K.-W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.43 · Cited by 14

Retrieval-Augmented Knowledge Integration into Language Models: A Survey

Chen, Y., Röder, D., Erker, J.-J., Hennig, L., Thomas, P., Möller, S., & Roller, R. (2024) · doi:10.18653/v1/2024.knowllm-1.5 · Cited by 6