Trong Huy Phan
Papers
1
Total Citations
55
H-Index
1
About
Trong Huy Phan is a computer vision researcher whose work addresses the critical challenge of class imbalance in object detection—a fundamental problem for real-world applications like autonomous driving, surveillance, and robotics. His most cited paper, "Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses" (2020, 55 citations), introduces a principled approach to improving detection accuracy when foreground objects are scarce relative to background. By reweighting cross entropy losses, Phan’s method enhances the performance of leading detectors such as Faster R-CNN, YOLO, and SSD without requiring architectural changes. This contribution is particularly valuable for safety-critical systems where rare objects—like pedestrians or obstacles—must be reliably identified. Phan’s work sits at the intersection of deep learning and practical deployment, offering solutions that are both theoretically sound and immediately applicable. With growing recognition in the vision community, his research continues to influence how models handle data imbalance, making object detection more robust and equitable across diverse real-world scenarios.
Research Focus
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Top Papers
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