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pro vyhledávání: '"Song, Weilian"'
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-learning algorithms in solving the few-shot classification problem, there
Externí odkaz:
http://arxiv.org/abs/2011.00179
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 applications to a variety of design, operation, planning, and policy decisions o
Externí odkaz:
http://arxiv.org/abs/2006.08021
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 vehicle speed in ideal conditions, without congestion or adverse weathe
Externí odkaz:
http://arxiv.org/abs/1906.10104
Autor:
Song, Weilian, Workman, Scott, Hadzic, Armin, Zhang, Xu, Green, Eric, Chen, Mei, Souleyrette, Reginald, Jacobs, Nathan
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), which rates roads from highest risk (1 star) to lowest (5 stars). Ob
Externí odkaz:
http://arxiv.org/abs/1901.06013
Autor:
Song, Weilian
Publikováno v:
Theses and Dissertations--Computer Science.
Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost o
Autor:
Song, Weilian
Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7029971bf914ae5d251b060d1bb6ac3
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