Haiyang Si

Papers

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About

Haiyang Si is a computer vision researcher whose work centers on the development of efficient deep learning architectures for real-time scene understanding. His most recognized contribution lies in the domain of real-time semantic segmentation, where he addresses one of the field's core challenges: achieving high accuracy without sacrificing computational efficiency — a balance critical for practical deployment in autonomous driving and robotics systems. Si's most impactful work, "Real-Time Semantic Segmentation via Multiply Spatial Fusion Network," proposes an innovative convolutional neural network architecture designed to capture rich spatial context while maintaining the speed demanded by real-world applications. This research has garnered over 50 citations across its publications, reflecting its relevance to both academic researchers and industry practitioners working on perception systems for intelligent vehicles and robotic platforms. His focus on multiply spatial fusion strategies demonstrates a nuanced understanding of how multi-scale feature integration can dramatically improve scene parsing performance. For students and researchers entering the fields of autonomous systems or computer vision, Si's work offers an accessible yet rigorous foundation for understanding how modern segmentation networks balance the competing demands of precision and real-time inference.