no code implementations • 25 May 2024 • Huizhou Chen, Jiangyi Wang, Yuxin Li, Na Zhao, Jun Cheng, Xulei Yang
3D environment recognition is essential for autonomous driving systems, as autonomous vehicles require a comprehensive understanding of surrounding scenes.
no code implementations • 18 Mar 2024 • Yuxuan Wang, Xuanyu Yi, Zike Wu, Na Zhao, Long Chen, Hanwang Zhang
However, this approach faces a critical issue of multi-view inconsistency, where the guidance images exhibit significant discrepancies across views, leading to mode collapse and visual artifacts of 3DGS.
1 code implementation • 10 Jan 2024 • Yucheng Han, Na Zhao, Weiling Chen, Keng Teck Ma, Hanwang Zhang
Our DPKE enriches the knowledge of limited training data, particularly unlabeled data, from two perspectives: data-perspective and feature-perspective.
1 code implementation • 20 Sep 2023 • Yating Xu, Na Zhao, Gim Hee Lee
Few-shot point cloud semantic segmentation aims to train a model to quickly adapt to new unseen classes with only a handful of support set samples.
1 code implementation • ICCV 2023 • Yating Xu, Conghui Hu, Na Zhao, Gim Hee Lee
Existing fully-supervised point cloud segmentation methods suffer in the dynamic testing environment with emerging new classes.
1 code implementation • 18 Dec 2022 • Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, Gim Hee Lee
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
no code implementations • 9 Dec 2022 • Yuyang Zhao, Na Zhao, Gim Hee Lee
In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns.
no code implementations • 26 Sep 2022 • Junjia Huang, Wei Ma, Rong Li, Na Zhao, Tao Zhou
Result: The mean absolute prediction error on the testing set was 0. 273-0. 257 for spherical equivalent, ranging from 0. 189-0. 160 to 0. 596-0. 473 if we consider different lengths of historical records and different prediction durations.
1 code implementation • 19 Jul 2022 • Hualian Sheng, Sijia Cai, Na Zhao, Bing Deng, Jianqiang Huang, Xian-Sheng Hua, Min-Jian Zhao, Gim Hee Lee
Since Intersection-over-Union (IoU) based optimization maintains the consistency of the final IoU prediction metric and losses, it has been widely used in both regression and classification branches of single-stage 2D object detectors.
2 code implementations • 6 Apr 2022 • Yuyang Zhao, Zhun Zhong, Na Zhao, Nicu Sebe, Gim Hee Lee
Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
Ranked #4 on Robust Object Detection on DWD
no code implementations • 14 Dec 2021 • Na Zhao, Gim Hee Lee
Deep learning-based approaches have shown remarkable performance in the 3D object detection task.
no code implementations • 1 Nov 2021 • Na Zhao, Zhen Long, Zhi-Dan Zhao, Jian Wang
This implies that URIR can effectively use knowledge graph to obtain better user codes and item codes, thereby obtaining better recommendation results.
1 code implementation • CVPR 2021 • Na Zhao, Tat-Seng Chua, Gim Hee Lee
These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training.
Few-shot 3D Point Cloud Semantic Segmentation Segmentation +1
1 code implementation • CVPR 2020 • Na Zhao, Tat-Seng Chua, Gim Hee Lee
The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations.
1 code implementation • 3 Sep 2019 • GuanXiong Luo, Na Zhao, Wenhao Jiang, Edward S. Hui, Peng Cao
Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction.
no code implementations • 27 Aug 2019 • Yang Liu, Runnan He, Kuanquan Wang, Qince Li, Qiang Sun, Na Zhao, Henggui Zhang
Heart disease is one of the most common diseases causing morbidity and mortality.
1 code implementation • 15 Aug 2019 • Na Zhao, Tat-Seng Chua, Gim Hee Lee
In this paper, we present the PS^2-Net -- a locally and globally aware deep learning framework for semantic segmentation on 3D scene-level point clouds.
no code implementations • Frontiers in Physiology 2018 • Runnan He, Kuanquan Wang, Na Zhao, Yang Liu, Yongfeng Yuan, Qince Li, Henggui Zhang
The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities.
Ranked #2 on Atrial Fibrillation Detection on MIT-BIH AF