no code implementations • 4 Feb 2024 • Han Fang, Zhihao Song, Paul Weng, Yutong Ban
Recently, deep reinforcement learning has shown promising results for learning fast heuristics to solve routing problems.
no code implementations • 3 Feb 2024 • Lianhao Yin, Yutong Ban, Jennifer Eckhoff, Ozanan Meireles, Daniela Rus, Guy Rosman
Understanding and anticipating intraoperative events and actions is critical for intraoperative assistance and decision-making during minimally invasive surgery.
no code implementations • 26 Oct 2023 • Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
no code implementations • 21 Mar 2023 • Noam Buckman, Shiva Sreeram, Mathias Lechner, Yutong Ban, Ramin Hasani, Sertac Karaman, Daniela Rus
FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers.
no code implementations • 10 Oct 2022 • Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
no code implementations • 27 Feb 2022 • Yutong Ban, Jennifer A. Eckhoff, Thomas M. Ward, Daniel A. Hashimoto, Ozanan R. Meireles, Daniela Rus, Guy Rosman
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
no code implementations • 10 May 2021 • Yutong Ban, Guy Rosman, Jennifer A. Eckhoff, Thomas M. Ward, Daniel A. Hashimoto, Taisei Kondo, Hidekazu Iwaki, Ozanan R. Meireles, Daniela Rus
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation.
2 code implementations • 28 Mar 2021 • Yihong Xu, Yutong Ban, Guillaume Delorme, Chuang Gan, Daniela Rus, Xavier Alameda-Pineda
Methodologically, we propose the use of image-related dense detection queries and efficient sparse tracking queries produced by our carefully designed query learning networks (QLN).
Ranked #11 on Multi-Object Tracking on MOT20 (using extra training data)
no code implementations • 1 Sep 2020 • Yutong Ban, Guy Rosman, Thomas Ward, Daniel Hashimoto, Taisei Kondo, Hidekazu Iwaki, Ozanan Meireles, Daniela Rus
With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases.
2 code implementations • CVPR 2020 • Yihong Xu, Aljosa Osep, Yutong Ban, Radu Horaud, Laura Leal-Taixe, Xavier Alameda-Pineda
In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.
Ranked #4 on Multi-Object Tracking on 2D MOT 2015
no code implementations • 28 Sep 2018 • Yutong Ban, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud
We propose a variational inference model which amounts to approximate the joint distribution with a factorized distribution.
1 code implementation • 2 May 2018 • Songyou Peng, Le Zhang, Yutong Ban, Meng Fang, Stefan Winkler
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.