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pro vyhledávání: '"Garg, Sparsh"'
Autor:
Qureshi, Mohammad Nomaan, Garg, Sparsh, Yandun, Francisco, Held, David, Kantor, George, Silwal, Abhisesh
Sim2Real transfer, particularly for manipulation policies relying on RGB images, remains a critical challenge in robotics due to the significant domain shift between synthetic and real-world visual data. In this paper, we propose SplatSim, a novel fr
Externí odkaz:
http://arxiv.org/abs/2409.10161
Autor:
Liang, Mingfu, Su, Jong-Chyi, Schulter, Samuel, Garg, Sparsh, Zhao, Shiyu, Wu, Ying, Chandraker, Manmohan
Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed percept
Externí odkaz:
http://arxiv.org/abs/2403.17373
Autor:
Shin, Inkyu, Tsai, Yi-Hsuan, Zhuang, Bingbing, Schulter, Samuel, Liu, Buyu, Garg, Sparsh, Kweon, In So, Yoon, Kuk-Jin
Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semanti
Externí odkaz:
http://arxiv.org/abs/2204.12667
Autor:
Kim, Dongwan, Tsai, Yi-Hsuan, Suh, Yumin, Faraki, Masoud, Garg, Sparsh, Chandraker, Manmohan, Han, Bohyung
With increasing applications of semantic segmentation, numerous datasets have been proposed in the past few years. Yet labeling remains expensive, thus, it is desirable to jointly train models across aggregations of datasets to enhance data volume an
Externí odkaz:
http://arxiv.org/abs/2202.14030
Akademický článek
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Publikováno v:
In Optik February 2020 202
