16 citations · 28 across the 4 of their papers we have counts for
8 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…
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…
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
Xiaofeng Liu, Zhenhua Guo, Site Li +4
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the c…
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
Xiaofeng Liu, B. V. K Vijaya Kumar, Chao Yang +2
This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogen…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…