26 citations · 86 across the 10 of their papers we have counts for
17 papers
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Bo Jiang, Shaoyu Chen, Xinggang Wang +7
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first en…
AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception
Shaoyu Chen, Xinggang Wang, Tianheng Cheng +4
Studying the inherent symmetry of data is of great importance in machine learning. Point cloud, the most important data format for 3D environmental perception, is naturally endowed…
Sparse Instance Activation for Real-Time Instance Segmentation
Tianheng Cheng, Xinggang Wang, Shaoyu Chen +5
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods hea…
Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation
Xinggang Wang, Zhaojin Huang, Bencheng Liao +3
Video object detection is a fundamental problem in computer vision and has a wide spectrum of applications. Based on deep networks, video object detection is actively studied for p…
Diversity Transfer Network for Few-Shot Learning
Mengting Chen, Yuxin Fang, Xinggang Wang +6
Few-shot learning is a challenging task that aims at training a classifier for unseen classes with only a few training examples. The main difficulty of few-shot learning lies in th…
RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
Shaoru Wang, Yongchao Gong, Junliang Xing +3
Object detection and instance segmentation are two fundamental computer vision tasks. They are closely correlated but their relations have not yet been fully explored in most previ…