Mingliang Gao

About

Mingliang Gao is a leading researcher in computer vision and robotics, with a primary focus on RGB-D object recognition and multimodal deep learning. His most-cited work, "RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey" (2019, 57 citations), provides a comprehensive analysis of how convolutional neural networks fuse color and depth data to improve object recognition in real-world environments. This survey has become a key reference for researchers developing robust vision systems that leverage both RGB and depth sensors. Gao’s contributions lie in systematically categorizing multimodal fusion strategies and identifying challenges such as sensor noise and domain adaptation, guiding subsequent advances in the field. His work is particularly impactful for applications in autonomous robotics, augmented reality, and human-computer interaction, where reliable object recognition under varying conditions is critical. By synthesizing a rapidly evolving research area, Gao has helped shape the direction of multimodal perception, making his survey an essential resource for students and practitioners alike.

Research Focus

Key Achievements

Top Papers

1. 1
   RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

   57 citations · 2019

Key Collaborators

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