no code implementations • 11 Apr 2023 • Hongwei Ren, Yuhong Shi, Kewei Liang
The most commonly used models for this task are autoregressive models, such as recurrent neural networks (RNNs) or variants, and Transformer Networks.
1 code implementation • 6 Feb 2023 • Qinrou Wen, Jirui Yang, Xue Yang, Kewei Liang
To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework.
1 code implementation • CVPR 2021 • Xing Shen, Jirui Yang, Chunbo Wei, Bing Deng, Jianqiang Huang, Xiansheng Hua, Xiaoliang Cheng, Kewei Liang
Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity.
no code implementations • 18 Oct 2020 • Chaobing Shan, Chunbo Wei, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Xiaoliang Cheng, Kewei Liang
It re-extracts the features of the tracklets in the current frame based on motion predicting, which is the key to solve the problem of features inconsistent.
no code implementations • 21 Mar 2020 • Xing Shen, Xiaoliang Cheng, Kewei Liang
In this paper, we propose a deep learning-based method, deep Euler method (DEM) to solve ordinary differential equations.