activity
20222024
most citedRSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments

9 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery

Ruifeng Li, Wei Liu, Xiangxin Zhou +4

In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular propert…

cs.CV2023

OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes

Tao Xie, Kun Dai, Siyi Lu +7

In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a uni…

cs.CV20234 cited

Pillar R-CNN for Point Cloud 3D Object Detection

Guangsheng Shi, Ruifeng Li, Chao Ma

The performance of point cloud 3D object detection hinges on effectively representing raw points, grid-based voxels or pillars. Recent two-stage 3D detectors typically take the poi…

cs.CV20233 cited

DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching

Tao Xie, Kun Dai, Ke Wang +2

Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes. In this work, we…

cs.CV20229 cited

RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments

Yafu Tian, Alexander Carballo, Ruifeng Li +1

Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems. Different from other research focused on trajectory, position, and bo…