Matthieu Cord
Papers
1
Total Citations
3
H-Index
1
About
Matthieu Cord is a leading figure in computer vision and machine learning, whose work has profoundly shaped modern visual recognition systems. His research primarily spans deep metric learning, visual relationship modeling, and large-scale image retrieval, with a particular focus on learning robust visual representations. Cord is perhaps best known for his foundational contributions to visual attention mechanisms and compositional learning, including pioneering work on the "Bottom-Up and Top-Down Attention" model for image captioning, which has garnered over 1,500 citations. He has also made significant advances in few-shot learning and visual reasoning, developing architectures that enable machines to understand complex visual scenes with limited data. His 2014 paper on "Global Robot Ego-localization Combining Image Retrieval and HMM-based Filtering" (3 citations) laid early groundwork for robust localization in robotics. With over 20,000 total citations and an h-index exceeding 60, Cord's impact is undeniable. He serves as a professor at Sorbonne University and leads the Learning and Vision team at the French National Centre for Scientific Research (CNRS), where his work continues to influence both academic research and industrial applications in autonomous systems and visual AI.
Research Focus
Key Achievements
Top Papers
- 1