26 citations · 47 across the 10 of their papers we have counts for
10 papers · 1 filter
Towards Robust Few-shot Point Cloud Semantic Segmentation
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. However, the noise-free assumpti…
Generalized Few-Shot Point Cloud Segmentation Via Geometric Words
Yating Xu, Conghui Hu, Na Zhao +1
Existing fully-supervised point cloud segmentation methods suffer in the dynamic testing environment with emerging new classes. Few-shot point cloud segmentation algorithms address…
Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds
Yuyang Zhao, Na Zhao, Gim Hee Lee
Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption th…
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
Yuyang Zhao, Zhun Zhong, Na Zhao +2
Domain shift widely exists in the visual world, while modern deep neural networks commonly suffer from severe performance degradation under domain shift due to the poor generalizat…
Rethinking IoU-based Optimization for Single-stage 3D Object Detection
Hualian Sheng, Sijia Cai, Na Zhao +5
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 class…
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
Yuyang Zhao, Zhun Zhong, Na Zhao +2
In this paper, we study the task of synthetic-to-real domain generalized semantic segmentation, which aims to learn a model that is robust to unseen real-world scenes using only sy…
Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection
Na Zhao, Gim Hee Lee
Deep learning-based approaches have shown remarkable performance in the 3D object detection task. However, they suffer from a catastrophic performance drop on the originally traine…
Few-shot 3D Point Cloud Semantic Segmentation
Na Zhao, Tat-Seng Chua, Gim Hee Lee
Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data tha…
SESS: Self-Ensembling Semi-Supervised 3D Object Detection
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. However, such annotations are often tedious and…
PS^2-Net: A Locally and Globally Aware Network for Point-Based Semantic Segmentation
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. In order to deeply incorpor…