18 citations · 35 across the 8 of their papers we have counts for
8 papers
Robust Graph Structure Learning via Multiple Statistical Tests
Yaohua Wang, FangYi Zhang, Ming Lin +3
Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is a…
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Yaohua Wang, Yaobin Zhang, Fangyi Zhang +4
Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Gr…
2nd Place Solution to Google Landmark Retrieval 2021
Zhang Yuqi, Xu Xianzhe, Chen Weihua +4
This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-iden…
Interpolation variable rate image compression
Zhenhong Sun, Zhiyu Tan, Xiuyu Sun +4
Compression standards have been used to reduce the cost of image storage and transmission for decades. In recent years, learned image compression methods have been proposed and ach…
Fine-Grained AutoAugmentation for Multi-Label Classification
Ya Wang, Hesen Chen, Fangyi Zhang +4
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better…
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering
Huafeng Yang, Qijie Shen, Xingjian Chen +2
In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attent…
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection
Cangning Fan, Fangyi Zhang, Peng Liu +5
Previous transfer methods for anomaly detection generally assume the availability of labeled data in source or target domains. However, such an assumption is not valid in most real…
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
Zhenhong Sun, Zhiyu Tan, Xiuyu Sun +4
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffer…