activity
20192023
most citedCombining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2023

A-Scan2BIM: Assistive Scan to Building Information Modeling

Weilian Song, Jieliang Luo, Dale Zhao +3

This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Mod…

cs.LG20201 cited

Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification

Shuman Peng, Weilian Song, Martin Ester

The goal of few-shot classification is to learn a model that can classify novel classes using only a few training examples. Despite the promising results shown by existing meta-lea…

cs.CV2020

RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery

Armin Hadzic, Hunter Blanton, Weilian Song +3

Roadway free-flow speed captures the typical vehicle speed in low traffic conditions. Modeling free-flow speed is an important problem in transportation engineering with applicatio…

cs.CV2019

Remote Estimation of Free-Flow Speeds

Weilian Song, Tawfiq Salem, Hunter Blanton +1

We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed v…

cs.CV2019

FARSA: Fully Automated Roadway Safety Assessment

Weilian Song, Scott Workman, Armin Hadzic +5

This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP)…