Wenzhan Li

Papers

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About

Wenzhan Li is a rising researcher in computer vision and autonomous systems, with a primary focus on pedestrian trajectory prediction and domain adaptation. Their most-cited work, "Dual-Alignment Domain Adaptation for Pedestrian Trajectory Prediction" (2024), addresses a critical gap in the field: the poor generalization of trajectory prediction models across different scenes. Li proposes a novel dual-alignment framework that aligns both feature-level and output-level distributions between source and target domains, enabling models trained on multi-scene data to perform reliably in unseen single-scene environments. This work is foundational for safety-critical applications like autonomous driving and service robotics, where accurate prediction of pedestrian paths is essential. With 4 citations in its first year, the paper signals growing recognition of Li’s contributions to domain adaptation in trajectory forecasting. By tackling the challenge of cross-scene generalization, Li is helping to bridge the gap between lab-trained models and real-world deployment. Their research promises to make human-involved AI systems safer and more robust, marking them as a promising voice in the next generation of vision and robotics researchers.