Search Results for author: Jingwei Liu

Found 6 papers, 1 papers with code

Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning

no code implementations29 Sep 2021 Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng

In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.

Data Augmentation Self-Supervised Learning

SARS-Cov-2 RNA Sequence Classification Based on Territory Information

no code implementations9 Jan 2021 Jingwei Liu

For 3-class classification of China, the Top-1 accuracy rate can reach 82. 45\% (train 60\%, test=40\%); For 2-class classification of China, the Top-1 accuracy rate can reach 97. 35\% (train 80\%, test 20\%); For 6-class classification task of world, when the ratio of training set and test set is 20\% : 80\% , the Top-1 accuracy rate can achieve 30. 30\%.

Classification General Classification

The 1st Tiny Object Detection Challenge:Methods and Results

1 code implementation16 Sep 2020 Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jiang, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi

The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.

Human Detection Object Detection

Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm

no code implementations19 Apr 2019 Jingwei Liu

Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) based microbial species classification with N-best algorithm is constructed to classify the centroid marker genome fragments randomly generated from marker genome sequences on MetaRef.

General Classification Time Series

Formula of Volume of Revolution with Integration by Parts and Extension

no code implementations4 Sep 2016 Yi Liu, Jingwei Liu

A calculation formula of volume of revolution with integration by parts of definite integral is derived based on monotone function, and extended to a general case that curved trapezoids is determined by continuous, piecewise strictly monotone and differential function.

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