105 citations · 369 across the 26 of their papers we have counts for
5 papers · 1 filter
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao +14
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda Uni…
Field-wise Learning for Multi-field Categorical Data
Zhibin Li, Jian Zhang, Yongshun Gong +2
We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect…
Data-driven Meta-set Based Fine-Grained Visual Classification
Chuanyi Zhang, Yazhou Yao, Xiangbo Shu +3
Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, lea…
Salvage Reusable Samples from Noisy Data for Robust Learning
Zeren Sun, Xian-Sheng Hua, Yazhou Yao +3
Due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images ten…
PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks
Benyi Hu, Ren-Jie Song, Xiu-Shen Wei +3
Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a u…