82 citations · 117 across the 8 of their papers we have counts for
8 papers
NormKD: Normalized Logits for Knowledge Distillation
Zhihao Chi, Tu Zheng, Hengjia Li +4
Logit based knowledge distillation gets less attention in recent years since feature based methods perform better in most cases. Nevertheless, we find it still has untapped potenti…
Learning Occupancy for Monocular 3D Object Detection
Liang Peng, Junkai Xu, Haoran Cheng +6
Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to fac…
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
Hengjia Li, Tu Zheng, Zhihao Chi +5
Transformer-based networks have achieved impressive performance in 3D point cloud understanding. However, most of them concentrate on aggregating local features, but neglect to dir…
DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection
Liang Peng, Xiaopei Wu, Zheng Yang +2
Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. T…
CLRNet: Cross Layer Refinement Network for Lane Detection
Tu Zheng, Yifei Huang, Yang Liu +4
Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern…
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Liang Peng, Senbo Yan, Boxi Wu +3
Monocular 3D object detection is one of the most challenging tasks in 3D scene understanding. Due to the ill-posed nature of monocular imagery, existing monocular 3D detection meth…
Region Mutual Information Loss for Semantic Segmentation
Shuai Zhao, Yang Wang, Zheng Yang +1
Semantic segmentation is a fundamental problem in computer vision. It is considered as a pixel-wise classification problem in practice, and most segmentation models use a pixel-wis…
Training-Time-Friendly Network for Real-Time Object Detection
Zili Liu, Tu Zheng, Guodong Xu +3
Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance among them, we propose the Training-Ti…