Search Results for author: Fei Hu

Found 12 papers, 6 papers with code

Vision-Based Human Pose Estimation via Deep Learning: A Survey

no code implementations26 Aug 2023 Gongjin Lan, Yu Wu, Fei Hu, Qi Hao

In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE.

Pose Estimation

SPCNet: Stepwise Point Cloud Completion Network

4 code implementations5 Sep 2022 Fei Hu, Honghua Chen, Xuequan Lu, Zhe Zhu, Jun Wang, Weiming Wang, Fu Lee Wang, Mingqiang Wei

We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.

Point Cloud Completion

Densely connected neural networks for nonlinear regression

1 code implementation29 Jul 2021 Chao Jiang, Canchen Jiang, Dongwei Chen, Fei Hu

Densely connected convolutional networks (DenseNet) behave well in image processing.

regression

A dynamical approach to generalized Weil's Riemann hypothesis and semisimplicity

no code implementations8 Feb 2021 Fei Hu, Tuyen Trung Truong

As an application, we obtain new results on the DDC conjecture for abelian varieties and Kummer surfaces, and the generalized semisimplicity conjecture for Kummer surfaces.

Algebraic Geometry Dynamical Systems Number Theory 14G17, 37P25, 14K05, 14J28, 14C25, 14F20

Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations

no code implementations24 Oct 2019 Fei Hu, Wei Liu, Ajmal Saeed Mian, Li Li

In this paper, we propose the Topic-coherent Hierarchical Recurrent Encoder-Decoder model (THRED) to diversify the generated responses without deviating the contextual topics for multi-turn conversations.

Response Generation

An Improved Historical Embedding without Alignment

no code implementations19 Oct 2019 Xiaofei Xu, Ke Deng, Fei Hu, Li Li

Our method outperformed three other popular methods in terms of the number of words correctly identified to have changed in meaning.

Word Embeddings

Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment

no code implementations4 Sep 2019 Alex Bozarth, Brendan Dwyer, Fei Hu, Daniel Jalova, Karthik Muthuraman, Nick Pentreath, Simon Plovyt, Gabriela de Queiroz, Saishruthi Swaminathan, Patrick Titzler, Xin Wu, Hong Xu, Frederick R. Reiss, Vijay Bommireddipalli

A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL).

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