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About

Yi Ge is a researcher whose work spans autonomous driving perception, 3D localization, and acoustic sensing for the Internet of Things. His most impactful contribution, "Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training" (2020, 54 citations), addresses a critical challenge in autonomous driving by improving semantic segmentation—the pixel-level classification of road scenes. By introducing a discrete Wasserstein loss, Ge’s approach enhances the accuracy of segmentation models beyond traditional cross-entropy methods, directly benefiting self-driving car perception and robotics. In 3D localization, Ge co-developed "SphereVLAD++" (2022, 19 citations), an attention-based descriptor that improves viewpoint-invariant feature extraction for large-scale navigation tasks like last-mile delivery and autonomous driving. Earlier, his work on "Cognitive Acoustic Analytics Service for Internet of Things" (2017, 14 citations) pioneered non-contact acoustic sensing for diagnosing inanimate objects, demonstrating the versatility of his research. With a growing citation record, Ge’s contributions are shaping safer autonomous systems and smarter IoT environments.