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Robust Autonomous Vehicle Pursuit Without Expert Steering Labels

IEEE Robotics and Automation Letters 2023年第10期8卷 6595-6602页

作者： Pan, Jiaxin Zhou, Changyao Gladkova, Mariia Khan, Qadeer Cremers, Daniel Technical University of Munich Computer Vision Group Garching85748 Germany Munich Data Science Institute Garching85748 Germany Munich Center for Machine Learning Munchen80333 Germany University of Oxford OxfordOX1 3AZ United Kingdom

In this work, we present a learning method for both lateral and longitudinal motion control of an ego-vehicle for the task of vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver, but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network that is capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance, robustness to different scenarios including unseen trajectories and high route completion. © 2016 IEEE.

关键词： Deep learning

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Self-supervised Sparse to Dense Motion Segmentation 15th

Self-supervised Sparse to Dense Motion Segmentation

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Kardoost, Amirhossein Ho, Kalun Ochs, Peter Keuper, Margret Data and Web Science Group University of Mannheim Mannheim Germany Fraunhofer Center Machine Learning Sankt Augustin Germany Fraunhofer ITWM Competence Center HPC Kaiserslautern Germany Mathematical Optimization Group Saarland University Saarbrcken Germany

ISBN: (纸本)9783030695316

Observable motion in videos can give rise to the definition of objects moving with respect to the scene. The task of segmenting such moving objects is referred to as motion segmentation and is usually tackled either by aggregating motion information in long, sparse point trajectories, or by directly producing per frame dense segmentations relying on large amounts of training data. In this paper, we propose a self supervised method to learn the densification of sparse motion segmentations from single video frames. While previous approaches towards motion segmentation build upon pre-training on large surrogate datasets and use dense motion information as an essential cue for the pixelwise segmentation, our model does not require pre-training and operates at test time on single frames. It can be trained in a sequence specific way to produce high quality dense segmentations from sparse and noisy input. We evaluate our method on the well-known motion segmentation datasets FBMS 59 and DAVIS16. © 2021, Springer Nature Switzerland AG.

关键词： Large dataset

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SepLL: Separating Latent Class Labels from Weak Supervision Noise

SepLL: Separating Latent Class Labels from Weak Supervision ...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Stephan, Andreas Kougia, Vasiliki Roth, Benjamin Research Group Data Mining and Machine Learning Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science Vienna Austria Faculty of Philological and Cultural Studies University of Vienna Vienna Austria

In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only. Both types of information are reflected to different degrees by all labeled instances. In contrast to previous works that aimed at correcting or removing wrongly labeled instances, we learn a branched deep model that uses all data as-is, but splits the labeling function information in the latent space. Specifically, we propose the end-to-end model SepLL which extends a transformer classifier by introducing a latent space for labeling function specific and task-specific information. The learning signal is only given by the labeling functions matches, no pre-processing or label model is required for our method. Notably, the task prediction is made from the latent layer without any direct task signal. Experiments on Wrench text classification tasks show that our model is competitive with the state-of-the-art, and yields a new best average performance. © 2022 Association for Computational Linguistics.

关键词： Classification (of information)

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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learning invariances with stationary subspace analysis

Learning invariances with stationary subspace analysis

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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Meinecke, Frank C. Von Bünau, Paul Kawanabe, Motoaki Müller, Klaus-R. Machine Learning Group Dept. Computer Science TU Berlin Franklinstr. 28/29 10587 Berlin Germany Intelligent Data Analysis Group Fraunhofer FIRST.IDA Kekuléstr. 7 12489 Berlin Germany

ISBN: (纸本)9781424444427

Recently, a novel subspace decomposition method, termed 'Stationary Subspace Analysis' (SSA), has been proposed by Bünau et al. [10]. SSA aims to find a linear projection to a lower dimensional subspace such that the distribution of the projected data does not change over successive epochs or sub-datasets. We show that by modifying the loss function and the optimization procedure we can obtain an algorithm that is both faster and more accurate. We discuss the problem of indeterminacies and provide a lower bound on the number of epochs that is needed. Finally, we show in an experiment with simulated image patches, that SSA can be used favourably in invariance learning. ©2009 IEEE.

关键词： Invariance

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Non-separable spatiotemporal brain hemodynamics contain neural information

Non-separable spatiotemporal brain hemodynamics contain neur...

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International Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, Held at Neural Information Processing, NIPS 2011

作者： Bießmann, Felix Murayama, Yusuke Logothetis, Nikos K. Müller, Klaus-Robert Meinecke, Frank C. Machine Learning Group Berlin Institute of Technology Germany Max-Planck Institute for Biological Cybernetics Tübingen Germany Division of Imaging Science and Biomedical Engineering University of Manchester United Kingdom Bernstein Center for Computational Neuroscience Berlin Germany

The goal of many functional Magnetic Resonance Imaging (fMRI) studies is to infer neural activity from hemodynamic signals. Classical fMRI analysis approaches assume a canonical hemodynamic response function (HRF), which is identical in every voxel. Canonical HRFs imply space-time separability. Many studies explored the relevance of non-separable HRFs. These studies were focusing on the relationship between stimuli or electroencephalographic data and fMRI data. It is not clear from these studies whether non-separable spatiotemporal dynamics of fMRI signals contain neural information. This study provides direct empirical evidence that non-separable spatiotemporal deconvolutions of multivariate fMRI time series predict intracortical neural signals better than standard canonical HRF models. Our results demonstrate that there is more neural information in fMRI signals than detected by most analysis methods. © 2012 Springer-Verlag.

关键词： Hemodynamics

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Pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

Pattern Discovery in an EEG Database of Depression Patients:...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

The ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： data mining

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Comparing zero-shot self-explanations with human rationales in text classification

arXiv

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arXiv 2024年

作者： Brandl, Stephanie Eberle, Oliver Center for Social Data Science University of Copenhagen Denmark Machine Learning Group Technische Universität Berlin Germany

Instruction-tuned LLMs are able to provide an explanation about their output to users by generating self-explanations. These do not require gradient computations or the application of possibly complex XAI methods. In this paper, we analyse whether this ability results in a good explanation. We evaluate self-explanations in the form of input rationales with respect to their plausibility to humans as well as their faithfulness to models. We study two text classification tasks: sentiment classification and forced labour detection, i.e., identifying pre-defined risk indicators of forced labour. In addition to English, we include Danish and Italian translations of the sentiment classification task and compare self-explanations to human annotations for all samples. To allow for direct comparisons, we also compute post-hoc feature attribution, i.e., layer-wise relevance propagation (LRP) and analyse 4 LLMs. We show that self-explanations align more closely with human annotations compared to LRP, while maintaining a comparable level of faithfulness. This finding suggests that self-explanations indeed provide good explanations for text classification. © 2024, CC BY.

关键词： Zero-shot learning

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167-PFlops deep learning for electron microscopy: From learning physics to atomic manipulation

167-PFlops deep learning for electron microscopy: From learn...

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2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018

作者： Patton, Robert M. Travis Johnston, J. Young, Steven R. Schuman, Catherine D. March, Don D. Potok, Thomas E. Rose, Derek C. Lim, Seung-Hwan Karnowski, Thomas P. Ziatdinov, Maxim A. Kalinin, Sergei V. Oak Ridge National Laboratory Oak RidgeTN37831-6085 United States Computational Data Analytics Group United States Geographic Information Science and Technology Group United States Imaging Signals and Machine Learning Group United States Institute for Functional Imaging of Materials United States Center for Nanophase Materials Sciences United States

ISBN: (纸本)9781538683842

An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available. © 2018 IEEE.

关键词： Genetic algorithms

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Towards Predicting Menstrual Cycle Phases Exploiting Paralinguistic Features 46

Towards Predicting Menstrual Cycle Phases Exploiting Paralin...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Spiesberger, Anika A. Mallol-Ragolta, Adria Triantafyllopoulos, Andreas Schuller, Björn W. Chi - Health Informatics Klinikum rechts der Isar Technical University of Munich Germany Glam - Group on Language Audio & Music Imperial College London United Kingdom Mcml - Munich Center for Machine Learning Germany Mdsi - Munich Data Science Institute Germany

ISBN: (纸本)9798350371499

As a growing number of people focus on understanding their bodies, the menstrual cycle and its impact on reproduction are gaining attention. Several studies have shown that the voice changes during the menstrual cycle. However, existing research primarily employs comparative analysis to detect these differences. This paper proposes using machine learning methods to analyse paralinguistic features extracted from women's voices for predicting menstrual cycle phases. We leverage available data recorded during the menstrual and late follicular phases of 44 naturally cycling women. Using eight paralinguistic features, we achieve an accuracy of 60 %, showcasing the feasibility of classifying those two phases using speech signals. We discuss implications and suggest future research avenues, such as the need to use personalised approaches. © 2024 IEEE.

关键词： Contrastive learning

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