Xiuhua Zhang

Papers

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About

Xiuhua Zhang is a rising researcher in computer vision, specializing in object detection and domain adaptation for intelligent systems. Her work addresses the critical challenge of open-world domain incremental object detection, where models must adapt to new visual domains without forgetting previously learned knowledge. Zhang’s most cited paper introduces a novel multinetwork mean distillation loss function, designed to stabilize learning across shifting data distributions—a key requirement for real-world applications like autonomous driving and robotics. This contribution, published in 2023, has already garnered 4 citations, signaling early impact in a rapidly evolving field. By tackling the problem of catastrophic forgetting in edge-intelligent terminals, Zhang’s research bridges the gap between high-accuracy detection networks and practical deployment in dynamic environments. Her work is particularly notable for its focus on open-world scenarios, where models encounter unseen classes and domain shifts—a frontier that pushes beyond traditional closed-set benchmarks. As a researcher committed to making object detection robust and adaptable, Xiuhua Zhang is laying foundational work for next-generation intelligent systems that learn continuously in the wild.