Alayrac, Jean-Baptiste

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

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 1,555

Gemma 3 Technical Report

Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,156

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

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips

Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsVideo Analysis and Summarization

Cited by 986

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

Domain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

Cited by 966

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

Miech, A., Alayrac, J.-B., Laptev, I., Sivic, J., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 149

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers

Hendricks, L. A., Mellor, J., Schneider, R., Alayrac, J.-B., & Nematzadeh, A. (2021) · Transactions of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 100

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

MedGemma Technical Report

Sellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 49

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 8