Search Results for author: Yutong Ban

Found 12 papers, 3 papers with code

INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer

no code implementations4 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.

Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery

no code implementations3 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.

Decision Making Knowledge Graphs

Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models

no code implementations26 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.

Autonomous Driving Data Augmentation

Infrastructure-based End-to-End Learning and Prevention of Driver Failure

no code implementations21 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.

Autonomous Vehicles

On the Forward Invariance of Neural ODEs

no code implementations10 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.

Autonomous Vehicles Collision Avoidance +2

Concept Graph Neural Networks for Surgical Video Understanding

no code implementations27 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.

Video Understanding

SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery

no code implementations10 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.

Decision Making Generative Adversarial Network

TransCenter: Transformers with Dense Representations for Multiple-Object Tracking

2 code implementations28 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)

Image Classification Multi-Object Tracking +4

Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows

no code implementations1 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.

Surgical phase recognition

How To Train Your Deep Multi-Object Tracker

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.

Multi-Object Tracking Multiple Object Tracking +1

Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers

no code implementations28 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.

Bayesian Inference Variational Inference +1

A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues

1 code implementation2 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.

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