12 citations · 14 across the 2 of their papers we have counts for
5 papers
Multi-Modal 3D Object Detection by Box Matching
Zhe Liu, Xiaoqing Ye, Zhikang Zou +5
Multi-modal 3D object detection has received growing attention as the information from different sensors like LiDAR and cameras are complementary. Most fusion methods for 3D detect…
SOOD: Towards Semi-Supervised Oriented Object Detection
Wei Hua, Dingkang Liang, Jingyu Li +4
Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approache…
CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
Dingkang Liang, Jiahao Xie, Zhikang Zou +3
Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsup…
Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network
Bo Ju, Zhikang Zou, Xiaoqing Ye +4
3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with r…
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Zhikang Zou, Xiaoqing Ye, Liang Du +6
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object…