111 citations · 539 across the 40 of their papers we have counts for
8 papers · 1 filter
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
Yan Wang, Junbo Yin, Wei Li +3
LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, th…
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
Xingping Dong, Jianbing Shen, Ling Shao
The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervis…
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
Junbo Yin, Jianbing Shen, Xin Gao +2
Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in m…
Semi-supervised 3D Object Detection with Proficient Teachers
Junbo Yin, Jin Fang, Dingfu Zhou +4
Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point c…
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Junbo Yin, Dingfu Zhou, Liangjun Zhang +4
Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose l…
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation
Hanqing Wang, Wei Liang, Jianbing Shen +2
Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of inst…