9 citations · 26 across the 8 of their papers we have counts for
8 papers
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection
Zhenyu Wang, Yali Li, Hengshuang Zhao +1
The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain…
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation
Zhenyu Wang, Yali Li, Taichi Liu +2
In the current state of 3D object detection research, the severe scarcity of annotated 3D data, substantial disparities across different data modalities, and the absence of a unifi…
Uni3DETR: Unified 3D Detection Transformer
Zhenyu Wang, Yali Li, Xi Chen +2
Existing point cloud based 3D detectors are designed for the particular scene, either indoor or outdoor ones. Because of the substantial differences in object distribution and poin…
Detecting Everything in the Open World: Towards Universal Object Detection
Zhenyu Wang, Yali Li, Xi Chen +4
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual…
Hybrid Physical Metric For 6-DoF Grasp Pose Detection
Yuhao Lu, Beixing Deng, Zhenyu Wang +3
6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of intelligent robot. To imitate human reasoning ability for grasping objects, data driv…
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?
Zhenyu Wang, Yali Li, Shengjin Wang
Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the p…
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
Zhenyu Wang, Yali Li, Ye Guo +1
Semi-supervised learning aims to leverage a large amount of unlabeled data for performance boosting. Existing works primarily focus on image classification. In this paper, we delve…
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
Zhenyu Wang, Yali Li, Ye Guo +2
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models. Pre…