16 citations · 35 across the 5 of their papers we have counts for
9 papers
Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving
Xiaofeng Liu, Yimeng Zhang, Xiongchang Liu +3
Semantic segmentation is important for many real-world systems, e.g., autonomous vehicles, which predict the class of each pixel. Recently, deep networks achieved significant progr…
Disentanglement for Discriminative Visual Recognition
Xiaofeng Liu
Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better g…
Towards Disentangled Representations for Human Retargeting by Multi-view Learning
Chao Yang, Xiaofeng Liu, Qingming Tang +1
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an id…
Unimodal-uniform Constrained Wasserstein Training for Medical Diagnosis
Xiaofeng Liu, Xu Han, Yukai Qiao +2
The labels in medical diagnosis task are usually discrete and successively distributed. For example, the Diabetic Retinopathy Diagnosis (DR) involves five health risk levels: no DR…
Conservative Wasserstein Training for Pose Estimation
Xiaofeng Liu, Yang Zou, Tong Che +4
This paper targets the task with discrete and periodic class labels ( pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regres…
Attention Control with Metric Learning Alignment for Image Set-based Recognition
Xiaofeng Liu, Zhenhua Guo, Jane You +1
This paper considers the problem of image set-based face verification and identification. Unlike traditional single sample (an image or a video) setting, this situation assumes the…