29 citations · 64 across the 7 of their papers we have counts for
7 papers
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
Xiang Li, Junbo Yin, Botian Shi +3
Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches ofte…
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes
Xiang Li, Lingjing Wang, Yi Fang
Recent research has seen numerous supervised learning-based methods for 3D shape segmentation and remarkable performance has been achieved on various benchmark datasets. These supe…
Physics Guided Machine Learning Methods for Hydrology
Ankush Khandelwal, Shaoming Xu, Xiang Li +5
Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generati…
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector
Lingjing Wang, Xiang Li, Yi Fang
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation. Unlike classical optimization-based methods, recent learning-…
One-Shot Object Detection without Fine-Tuning
Xiang Li, Lin Zhang, Yau Pun Chen +2
Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich s…
Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks
Xiang Li, Jiechao Ma, Hongwei Li
Early diagnosis of pathological invasiveness of pulmonary adenocarcinomas using computed tomography (CT) imaging would alter the course of treatment of adenocarcinomas and subseque…
Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images
Jiechao Ma, Xiang Li, Hongwei Li +4
Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed…