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Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking

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arXiv 2022年

作者： Wang, Bisheng Possegger, Horst Bischof, Horst Cao, Guo School of Computer Science and Engerring Nanjing University of Science and Technology China Institute of Computer Graphics and Vision Graz University of Technology Austria

Existing Multiple Object Tracking (MOT) methods design complex architectures for better tracking performance. However, without a proper organization of input information, they still fail to perform tracking robustly and suffer from frequent identity switches. In this paper, we propose two novel methods together with a simple online Message Passing Network (MPN) to address these limitations. First, we explore different integration methods for the graph node and edge embeddings and put forward a new IoU (Intersection over Union) guided function, which improves long term tracking and handles identity switches. Second, we introduce a hierarchical sampling strategy to construct sparser graphs which allows to focus the training on more difficult samples. Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. In addition, our association method generalizes well and can also improve the results of private detection based methods. © 2022, CC BY-NC-SA.

关键词： Message passing

Product of Gaussian Mixture Diffusion Models

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arXiv 2023年

作者： Zach, Martin Kobler, Erich Chambolle, Antonin Pock, Thomas Institute of Computer Graphics and Vision Graz University of Technology Austria Klinik für Neuroradiologie Universitätsklinikum Bonn Germany and Mokaplan INRIA Paris France

In this work we tackle the problem of estimating the density fX of a random variable X by successive smoothing, such that the smoothed random variable Y fulfills the diffusion partial differential equation (∂t − ∆1)fY (·, t) = 0 with initial condition fY (·, 0) = fX. We propose a product-of-experts-type model utilizing Gaussian mixture experts and study configurations that admit an analytic expression for fY (·, t). In particular, with a focus on image processing, we derive conditions for models acting on filter-, wavelet-, and shearlet-responses. Our construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show numerical results for image denoising where our models are competitive while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our models can be used for reliable noise level estimation, allowing blind denoising of images corrupted by heteroscedastic noise. © 2023, CC BY.

关键词： Random variables

An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation

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arXiv 2024年

作者： Bogensperger, Lea Ehrhardt, Matthias J. Pock, Thomas Salehi, Mohammad Sadegh Wong, Hok Shing Department of Quantitative Biomedicine University of Zurich Zurich8057 Switzerland Department of Mathematical Sciences University of Bath BathBA2 7AY United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria

We consider a bilevel learning framework for learning linear operators. In this framework, the learnable parameters are optimized via a loss function that also depends on the minimizer of a convex optimization problem (denoted lower-level problem). We utilize an iterative algorithm called ‘piggyback’ to compute the gradient of the loss and minimizer of the lower-level problem. Given that the lower-level problem is solved numerically, the loss function and thus its gradient can only be computed inexactly. To estimate the accuracy of the computed hypergradient, we derive an a-posteriori error bound, which provides guides for setting the tolerance for the lower-level problem, as well as the piggyback algorithm. To efficiently solve the upper-level optimization, we also propose an adaptive method for choosing a suitable step-size. To illustrate the proposed method, we consider a few learned regularizer problems, such as training an input-convex neural network. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

MATE: Masked Autoencoders are Online 3D Test-Time Learners

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MATE: Masked Autoencoders are Online 3D Test-Time Learners

International Conference on computer vision (ICCV)

作者： M. Jehanzeb Mirza Inkyu Shin Wei Lin Andreas Schriebl Kunyang Sun Jaesung Choe Mateusz Kozinski Horst Possegger In So Kweon Kuk-Jin Yoon Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Korea Advanced Institute of Science and Technology (KAIST) South Korea Southeast University China

Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: a large portion of each test point cloud is removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. We show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, making it extremely lightweight. Our experiments show that MATE also achieves competitive performance by adapting sparsely on the test data, which further reduces its computational overhead, making it ideal for real-time applications.

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DRT: Detection Refinement for Multiple Object Tracking 32

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DRT: Detection Refinement for Multiple Object Tracking

32nd British Machine vision Conference, BMVC 2021

作者： Wang, Bisheng Fruhwirth-Reisinger, Christian Possegger, Horst Bischof, Horst Cao, Guo School of Computer Science and Engineering Nanjing University of Science and Technology China Christian Doppler Laboratory for Embedded Machine Learning Austria Institute of Computer Graphics and Vision Graz University of Technology Austria

Deep learning methods have led to remarkable progress in multiple object tracking (MOT). However, when tracking in crowded scenes, existing methods still suffer from both inaccurate and missing detections. This paper proposes Detection Refinement for Tracking (DRT) to address these two issues for people tracking. First, we construct an encoder-decoder backbone network with a novel semi-supervised heatmap training procedure, which leverages human heatmaps to obtain a more precise localization of the targets. Second, we integrate a "one patch, multiple predictions" mechanism into DRT which refines the detection results and recovers occluded pedestrians at the same time. Additionally, we leverage a data-driven LSTM-based motion model which can recover lost targets at a negligible computational cost. Compared with strong baseline methods, our DRT achieves significant improvements on publicly available MOT datasets. In addition, DRT generalizes well, i.e. it can be applied to any detector to improve their performance. © 2021. The copyright of this document resides with its authors.

关键词： Object detection

Bigger Isn’t Always Better: Towards a General Prior for Medical Image Reconstruction

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arXiv 2025年

作者： Glaszner, Lukas Zach, Martin Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Biomedical Imaging Group École polytechnique fédérale de Lausanne Lausanne1015 Switzerland Center for Biomedical Imaging Lausanne1015 Switzerland

Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate that they can be used in both MRI and CT reconstruction, out-performing model trained on medical images in out-of-distribution cases. As a result of our findings, we strongly caution against simply re-using very large networks and encourage researchers to adapt the model complexity to the respective task. Moreover, we argue that a key step towards a general diffusion-based prior is training on natural images. © 2025, CC BY-SA.

关键词： Medical imaging

Sparse Convolutional Neural Networks for Medical Image Analysis

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TechRxiv

TechRxiv 2022年

作者： Li, Jianning Gsaxner, Christina Pepe, Antonio Schmalstieg, Dieter Kleesiek, Jens Egger, Jan Institute for Artificial Intelligence in Medicine Essen University Hospital Germany Institute of Computer Graphics and Vision Graz University of Technology Austria

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of large and sparse medical images. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes only on the non-empty voxels, thus reducing the memory and computation overhead caused by the sparse input data. We evaluate our method on two clinically relevant skull reconstruction tasks: (1) given a defective skull, reconstruct the complete skull (i.e., skull shape completion), and (2) given a coarse skull, reconstruct a high-resolution skull with fine geometric details (shape super-resolution). Our method outperforms the state of the art in the skull reconstruction task quantitatively and qualitatively, while requiring substantially less memory for training and inference. We observed that, on the 3D skull data, the overall memory consumption of the sparse CNN grows approximately linearly during inference with respect to the image resolutions. During training, the memory usage remains clearly below increases in image resolution - an ×8 increase in voxel number leads to less than ×4 increase in memory requirements. Our study demonstrates the effectiveness of using a sparse CNN for skull reconstruction tasks, and our findings can be applied to other spatially sparse problems. We proof this by additional experimental results on other sparse medical datasets, like the aorta and the heart. Project page at https://***/Jianningli/SparseCNN. © 2022, CC BY.

关键词： Convolutional neural networks

The Hololens In Medicine: A Systematic Review and Taxonomy

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arXiv 2022年

作者： Gsaxner, Christina Li, Jianning Pepe, Antonio Jin, Yuan Kleesiek, Jens Schmalstieg, Dieter Egger, Jan Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Institute of AI in Medicine University Medicine Essen Essen45131 Germany

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research. In medical settings, the HoloLens enables the physician to obtain immediate insight into patient information, directly overlaid with their view of the clinical scenario, the medical student to gain a better understanding of complex anatomies or procedures, and even the patient to execute therapeutic tasks with improved, immersive guidance. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021, were attention is shifting towards it’s successor, the HoloLens 2. We identified 171 relevant publications through a systematic search of the PubMed and Scopus databases. We analyze these publications in regard to their intended use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation. We find that, although the feasibility of using the HoloLens in various medical scenarios has been shown, increased efforts in the areas of precision, reliability, usability, workflow and perception are necessary to establish AR in clinical practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

Enforcing connectivity of 3D linear structures using their 2D projections

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arXiv 2022年

作者： Oner, Doruk Osman, Hussein Kozinski, Mateusz Fua, Pascal Computer Vision Laboratory École Polytechnique Fédérale de Lausanne Switzerland Institute of Computer Graphics and Vision Graz University of Technology Austria

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture the topological properties of these structures. As a result, the connectivity of the recovered structures is often wrong, which lessens their usefulness. In this paper, we propose to improve the 3D connectivity of our results by minimizing a sum of topology-aware losses on their 2D projections. This suffices to increase the accuracy and to reduce the annotation effort required to provide the required annotated training data. Code is available at https://***/doruk-oner/ConnectivityOnProjections. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology