most citedDRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation

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

collaborators

2 papers

cs.CV2021★ 1 cited

DRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation

Haruya Sakashita, Christoph Flothow, Noriko Takemura +1

Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environment…

physics.data-an2021

Development of a Vertex Finding Algorithm using Recurrent Neural Network

Kiichi Goto, Taikan Suehara, Tamaki Yoshioka +5

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vert…