Search Results for author: Jin Jin

Found 8 papers, 6 papers with code

Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End

no code implementations5 Oct 2023 Jin Jin, Chong Zhang, Jonas Frey, Nikita Rudin, Matias Mattamala, Cesar Cadena, Marco Hutter

In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot.

Anomaly Detection Navigate +1

NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data

no code implementations16 Jul 2021 Francesco Cognolato, Kieran O'Brien, Jin Jin, Simon Robinson, Frederik B. Laun, Markus Barth, Steffen Bollmann

NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.

DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression

1 code implementation10 Aug 2020 Changhong Fu, Fangqiang Ding, Yiming Li, Jin Jin, Chen Feng

By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.

Real-Time Visual Tracking regression

Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization

1 code implementation10 Aug 2020 Fangqiang Ding, Changhong Fu, Yiming Li, Jin Jin, Chen Feng

Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement.

Translation Visual Tracking

Multi-resolution Super Learner for Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI

1 code implementation2 Jul 2020 Jin Jin, Lin Zhang, Ethan Leng, Gregory J. Metzger, Joseph S. Koopmeiners

While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the specific structures of the mpMRI data, such as the regional heterogeneity and between-voxel correlation within a subject.

BIG-bench Machine Learning

Bayesian Methods for the Analysis of Early-Phase Oncology Basket Trials with Information Borrowing across Cancer Types

1 code implementation7 Feb 2020 Jin Jin, Marie-Karelle Riviere, Xiaodong Luo, Yingwen Dong

We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach and Liu's two-stage approach.

Applications

Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks

1 code implementation19 Nov 2019 Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, Steffen Bollmann

Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion.

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