Yu-Ming Tang
Papers
1
Total Citations
2
H-Index
1
About
Yu-Ming Tang is a leading researcher in the field of procedural activity understanding, with a particular focus on error detection in augmented reality (AR) and robotic systems. Their work addresses a critical challenge: ensuring consistent and correct task execution by identifying deviations from normal action sequences. Tang’s major contribution lies in developing novel frameworks that move beyond static prototypes or simple temporal ordering checks. Instead, they model multiple normal action representations, capturing the inherent variability in how tasks can be correctly performed. This approach significantly improves the robustness and accuracy of error detection in complex, real-world procedural tasks. With their 2025 paper, "Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks," already garnering 2 citations shortly after publication, Tang’s research is gaining rapid recognition. Their work is pivotal for advancing autonomous systems and AR-assisted guidance, making them a key figure in bridging computer vision, human-computer interaction, and robotics. Tang’s innovative perspective promises to shape future standards for reliable task execution in dynamic environments.
Research Focus
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Top Papers
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