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About

Michael Ying Yang is a prominent computer vision and robotics researcher whose work spans semantic segmentation, autonomous systems, trajectory prediction, and scene understanding. He is perhaps best known for creating the UAVid dataset, a large-scale benchmark for semantic segmentation of UAV imagery that has garnered over 367 citations since 2020, making it an essential resource for aerial perception research in robotics and autonomous driving communities worldwide. Yang's contributions extend across multiple cutting-edge domains. His work on cascaded deep networks for infrared image super-resolution (103 citations) addressed critical challenges in surveillance and night-vision applications, while his GATraj model introduced an efficient graph- and attention-based framework for multi-agent trajectory prediction, directly tackling real-time constraints in autonomous driving. Earlier contributions to kinematic chain pose estimation and RGB-D pedestrian detection demonstrate his sustained engagement with robot perception challenges over nearly a decade. More recently, Yang has pushed into emerging frontiers including event camera-based visual odometry for legged robots and LLM-enhanced 3D indoor scene synthesis. With a cumulative citation record exceeding 640 across his most notable works, Yang has established himself as a versatile and influential voice in computer vision research, consistently bridging foundational dataset creation with practical, deployable intelligent systems.