no code implementations • 6 Feb 2024 • Liwei Yin, Yongjiang Shu, Heng Zhang, Yuefei Dai, Xiaopeng Lu, Yunlong Lian, Zhonghua Wang, Yong Ding
Reasonable vibration reduction design is an important way to achieve low phase noise index of airborne frequency source output signal.
no code implementations • 13 Jan 2024 • Yu Hong, Qian Liu, Huayuan Cheng, Danjiao Ma, Hang Dai, Yu Wang, Guangzhi Cao, Yong Ding
The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving.
1 code implementation • CVPR 2023 • Jiale Li, Hang Dai, Hao Han, Yong Ding
We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity.
1 code implementation • 14 Nov 2022 • Yu Hong, Hang Dai, Yong Ding
Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e. g., Pseudo-LiDAR methods.
Ranked #1 on Monocular 3D Object Detection on KITTI Cyclist Hard (using extra training data)
1 code implementation • CVPR 2022 • Yi-Nan Chen, Hang Dai, Yong Ding
Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image.
Ranked #1 on Monocular 3D Object Detection on KITTI Cars Moderate (AP metric)
no code implementations • 26 Dec 2021 • Chengjun Tang, Kun Zhang, Chunfang Xing, Yong Ding, Zengmin Xu
Combined with the defensive idea of adversarial training, we use Perlin noise to train the neural network to obtain a model that can defend against procedural noise adversarial examples.
1 code implementation • 8 Aug 2021 • Jiale Li, Hang Dai, Ling Shao, Yong Ding
In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder.
2 code implementations • 8 Aug 2021 • Jiale Li, Hang Dai, Ling Shao, Yong Ding
We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention.
1 code implementation • CVPR 2021 • Shujie Luo, Hang Dai, Ling Shao, Yong Ding
In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores.
no code implementations • 10 Apr 2020 • Jiale Li, Shujie Luo, Ziqi Zhu, Hang Dai, Andrey S. Krylov, Yong Ding, Ling Shao
In order to obtain a more accurate IoU prediction, we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation.