99 citations · 283 across the 17 of their papers we have counts for
17 papers
Structure-Preserving Graph Kernel for Brain Network Classification
Jun Yu, Zhaoming Kong, Aditya Kendre +5
This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the…
BiSTF: Bilateral-Branch Self-Training Framework for Semi-Supervised Large-scale Fine-Grained Recognition
Hao Chang, Guochen Xie, Jun Yu +1
Semi-supervised Fine-Grained Recognition is a challenge task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recent years, this field has…
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism
Fang Gao, Jiabao Wang, Jun Yu +2
Monocular depth estimation (MDE) is a fundamental task in many applications such as scene understanding and reconstruction. However, most of the existing methods rely on accurately…
The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation
Jun Bao, Buyu Liu, Jun Yu
We propose a novel method on refining cross-person gaze prediction task with eye/face images only by explicitly modelling the person-specific differences. Specifically, we first as…
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise, amo…
Instance Correction for Learning with Open-set Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
The problem of open-set noisy labels denotes that part of training data have a different label space that does not contain the true class. Lots of approaches, e.g., loss correction…