most citedAssociate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection

15 citations · 25 across the 4 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2023

A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Yibo Zhou, Hai-Miao Hu, Jinzuo Yu +3

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is…

cs.CV2023

One-shot neural band selection for spectral recovery

Hai-Miao Hu, Zhenbo Xu, Wenshuai Xu +5

Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit cluste…

cs.CV20202 cited

Segment as Points for Efficient Online Multi-Object Tracking and Segmentation

Zhenbo Xu, Wei Zhang, Xiao Tan +5

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-detection paradigm and adopt convolutions for feature extraction. However, as affected by the i…

cs.CV20204 cited

PointTrack++ for Effective Online Multi-Object Tracking and Segmentation

Zhenbo Xu, Wei Zhang, Xiao Tan +7

Multiple-object tracking and segmentation (MOTS) is a novel computer vision task that aims to jointly perform multiple object tracking (MOT) and instance segmentation. In this work…

cs.CV202015 cited

Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection

Liang Du, Xiaoqing Ye, Xiao Tan +4

Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion…

cs.CV20204 cited

ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

Zhenbo Xu, Wei Zhang, Xiaoqing Ye +6

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded o…