Research Interests

• Machine Learning
• Computer Vision
• Few-shot Learning
• Large Model

Paper

• Liu Q, Zhao Z, Cao W, et al. Residual proportion multilayer perceptron for few-shot classification[C]//2021 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2021: 1-6.
• Liu Q, Cao W. Geometric algebra graph neural network for cross-domain few-shot classification[J]. Applied Intelligence, 2022, 52(11): 12422-12435.
• Zhao Z, Liu Q, Cao W, et al. Self-guided information for few-shot classification[J]. Pattern Recognition, 2022, 131: 108880.
• Liu Q, Chen Y, Cao W. Dual-domain reciprocal learning design for few-shot image classification[J]. Neural Computing and Applications, 2023: 1-14.
• Liu Q, Cao W, He Z. Cycle Optimization Metric Learning for Few-Shot Classification[J]. Pattern Recognition, 2023: 109468.
• Zhang R, Liu Q. Learning with few samples in deep learning for image classification, a mini-review[J]. Frontiers in Computational Neuroscience, 2023.

Patent

*Vehicle detection method, device and computer-readable storage medium, He Zhiquan, Liu Qifan, Cao Wenming, patent number: ZL 2019 1 0029834.8 *Progressive deep convolutional network image recognition method and device based on wavelet transform, He Zhiquan, Cao Wenming, Liu Qifan, patent number: ZL 2019 1 0783600.2 *Compression transmission method, system and computer-readable storage medium for image data, He Zhiquan, Cao Wenming, Liu Qifan, patent number: ZL 2019 1 0811971.7 *An OCR recognition method and device based on deep neural network, Cao Wenming, Liu Qifan, He Zhiquan, patent number: ZL 2019 1 0904514.2