Carlos Garrido-Munoz
Papers
1
Total Citations
2
H-Index
1
About
Carlos Garrido-Munoz is a researcher focused on the intersection of computer vision and domain generalization, with a particular emphasis on practical, real-world applications. His work tackles the critical challenge of training machine learning models that perform reliably across diverse, unseen environments—a problem central to deploying AI in dynamic settings. His most cited paper, "Evaluating Domain Generalization in Kitchen Utensils Classification" (2023), exemplifies this focus by systematically testing how well models trained on standard datasets can adapt to novel kitchen contexts, a task that mirrors the variability of everyday life. This contribution, while early in its citation trajectory, has already garnered attention for its rigorous methodology and its potential to improve robotic perception and assistive technologies. Garrido-Munoz’s research is notable for bridging the gap between theoretical domain adaptation and tangible, object-level classification tasks, offering insights that could enhance the robustness of AI in household and industrial environments. His work underscores a commitment to making machine learning systems more adaptable and trustworthy, a pursuit that positions him as an emerging voice in the field of applied computer vision.
Research Focus
Key Achievements
Top Papers
- 1Evaluating Domain Generalization in Kitchen Utensils Classification2 citations · 2023