Zobrazeno 1 - 10
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pro vyhledávání: '"Hurtado, Juana Valeria"'
Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasting; however, they do not consider the geometry of the scene. In this w
Externí odkaz:
http://arxiv.org/abs/2409.12008
Deep learning has led to remarkable strides in scene understanding with panoptic segmentation emerging as a key holistic scene interpretation task. However, the performance of panoptic segmentation is severely impacted in the presence of out-of-distr
Externí odkaz:
http://arxiv.org/abs/2310.11797
Autor:
Londoño, Laura, Hurtado, Juana Valeria, Hertz, Nora, Kellmeyer, Philipp, Voeneky, Silja, Valada, Abhinav
Machine learning has significantly enhanced the abilities of robots, enabling them to perform a wide range of tasks in human environments and adapt to our uncertain real world. Recent works in various machine learning domains have highlighted the imp
Externí odkaz:
http://arxiv.org/abs/2207.03444
As different research works report and daily life experiences confirm, learning models can result in biased outcomes. The biased learned models usually replicate historical discrimination in society and typically negatively affect the less represente
Externí odkaz:
http://arxiv.org/abs/2201.10853
Autor:
Fong, Whye Kit, Mohan, Rohit, Hurtado, Juana Valeria, Zhou, Lubing, Caesar, Holger, Beijbom, Oscar, Valada, Abhinav
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of the scene, performing these tas
Externí odkaz:
http://arxiv.org/abs/2109.03805
Publikováno v:
IEEE/ CVF International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11612-11621, 2021
Attributes of sound inherent to objects can provide valuable cues to learn rich representations for object detection and tracking. Furthermore, the co-occurrence of audiovisual events in videos can be exploited to localize objects over the image fiel
Externí odkaz:
http://arxiv.org/abs/2103.01353
Publikováno v:
Frontiers in Robotics and AI, 2021
The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to ma
Externí odkaz:
http://arxiv.org/abs/2101.02647
Publikováno v:
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Scalability in Autonomous Driving, 2020
Comprehensive understanding of dynamic scenes is a critical prerequisite for intelligent robots to autonomously operate in their environment. Research in this domain, which encompasses diverse perception problems, has primarily been focused on addres
Externí odkaz:
http://arxiv.org/abs/2004.08189
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