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Yunqi Miao
Researcher at University of Warwick
Publications - 13
Citations - 175
Yunqi Miao is an academic researcher from University of Warwick. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 4, co-authored 9 publications receiving 64 citations. Previous affiliations of Yunqi Miao include University of Electronic Science and Technology of China & Lancaster University.
Papers
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Shallow feature based dense attention network for crowd counting
TL;DR: A Shallow feature based Dense Attention Network (SDANet) is proposed for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features.
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ST-CNN: Spatial-Temporal Convolutional Neural Network for crowd counting in videos
TL;DR: Experimental results show that the proposed Spatial-Temporal Convolutional Neural Network outperforms the state-of-the-art models in terms of mean absolutely error (MAE) and mean squared error (MSE).
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Shallow Feature Based Dense Attention Network for Crowd Counting
TL;DR: Zhang et al. as discussed by the authors proposed a shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of background via involving shallow feature-based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features.
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Diffusion and separation mechanism of transient electromagnetic and thermal fields
TL;DR: In this paper, a physical-mathematical time-dependent partition model is proposed to analyze the thermal transient process and consider characteristic times for separating Joule heating and thermal diffusion into four different stages.
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Learning Transformation-Invariant Local Descriptors With Low-Coupling Binary Codes
TL;DR: In this paper, the transformation invariance of binary local descriptors is ensured by projecting the original patches and their transformed counterparts into an identical high-dimensional feature space and an identical low-dimensional descriptor space simultaneously.