Cheng-Shan Jiang
Papers
4
Total Citations
48
H-Index
4
About
Cheng-Shan Jiang is a leading researcher at the intersection of computer vision and human–robot interaction (HRI), with a primary focus on advancing facial expression recognition (FER) and visual emotion recognition. His work addresses critical challenges in making intelligent machines more empathetic and responsive to human emotional states. Jiang’s major contributions include pioneering the development of efficient, robust deep learning architectures that balance computational efficiency with high recognition performance. He has introduced innovative approaches such as hierarchical co-consistency quantization for binary neural networks, zero-addition pretext training strategies, and multi-scale convolutional vision transformers. His most cited paper (2024, 21 citations) tackles FER in HRI by proposing a novel binary network that overcomes the robustness and efficiency limitations of conventional CNNs. His 2023 work on feature conjunction-selection networks (15 citations) further advances hybrid feature extraction. With recent publications in 2025 exploring motion semantic enhancement for static-dynamic emotion recognition, Jiang continues to push the boundaries of affective computing, making his research highly relevant for students and engineers developing next-generation social robots and emotionally intelligent AI systems.
Research Focus
Key Achievements
Top Papers
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