8 Papers
13 Citations
Qiuyu Chen is an academic researcher from University of North Carolina at Charlotte. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 3, co-authored 8 publications.
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Papers
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Qiuyu Chen,Wei Zhang,Ning Zhou,Peng Lei,Yi Xu,Yu Zheng,Jianping Fan +6 more
- 14 Jun 2020
TL;DR: In this paper, an adaptive fractional dilated convolution (AFDC) is proposed to incorporate the information of image aspect ratios to learn more robust models, where the interpolation of nearest two integer dilated kernels are used to cope with the misalignment of fractional sampling.
•Posted Content
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
TL;DR: An adaptive fractional dilated convolution (AFDC) is developed, which is aspect-ratio-embedded, composition-preserving and parameter-free, which can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner.
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Hierarchical convolutional neural network via hierarchical cluster validity based visual tree learning
TL;DR: The experimental results have demonstrated that the proposed hierarchical cluster validity index (HCVI) can guide the building of a more reasonable visual tree structure, so that the hierarchical convolutional neural network can achieve better results on classification accuracy.
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Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
TL;DR: A deep mixture of diverse experts algorithm is developed to achieve more efficient learning of a huge (mixture) network for large-scale visual recognition application and can achieve very competitive results on large- scale visual recognition.
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•Proceedings Article
GIF Thumbnails: Attract More Clicks to Your Videos
Yi Xu,Fan Bai,Yingxuan Shi,Qiuyu Chen,Longwen Gao,Kai Tian,Shuigeng Zhou,Huyang Sun +7 more
- 18 May 2021
TL;DR: Zhang et al. as mentioned in this paper proposed a generative variational dual-encoder (GEVADEN) model to generate GIF thumbnails for videos and boost their Click-Through-Rate (CTR).