34 citations · 213 across the 28 of their papers we have counts for
5 papers · 1 filter
Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training
Xiaofeng Liu, Yuzhuo Han, Song Bai +6
Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entr…
Mutual Information Regularized Identity-aware Facial ExpressionRecognition in Compressed Video
Xiaofeng Liu, Linghao Jin, Xu Han +1
How to extract effective expression representations that invariant to the identity-specific attributes is a long-lasting problem for facial expression recognition (FER). Most of th…
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
Xiaofeng Liu, Tong Che, Yiqun Lu +3
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-t…
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…