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Transformers for Orbit Determination Anomaly Detection and Classification

Transformers for Orbit Determination Anomaly Detection and C...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nathan Parrish Ré Matthew Popplewell Michael Caudill Timothy Sullivan Tyler Hanf Benjamin Tatman Kanak Parmar Tyler Presser Sai Chikine Michael Grant Richard Poulson Advanced Space LLC Westminster CO

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper presents the judicious application of machine learning algorithms to solve two fundamental challenges in spacecraft orbit determination (OD): identification of systematic anomalies (nominal vs anomalous behavior) and the classification of these anomalies to explain probable causes (such as unexpected spacecraft maneuvers or mis-modeled small forces). Traditional OD is based on well-tested iterative linearization methods (variations of the Kalman filter). These are commonly understood in the astrodynamics community to require manual tuning for a given set of mission assumptions and re-tuning when those assumptions are invalidated. OD data are typically long, sparse, multi-variate sequences consisting of multiple observation phenomenologies. These characteristics make the OD problem fundamentally well-suited to the same machine learning architectures (namely, transformers) that have found success with language modeling and other sequence-based data modeling. The approach taken here is to simulate various failure modes in the traditional OD process using NASA’s Monte software, then process the simulated data in three different transformer-based architectures. The three transformer architectures all act as classifiers and can be described at a high level as: 1) treat each epoch with data as a feature vector, with the input data comprising a single, long sequence; 2) similar to the first, but with nested self-attention to efficiently handle longer sequences; and 3) plot the data, then classify the plot with a vision transformer (ViT) model. Model performance is studied as a function of the hyperparameter trade space.

关键词： Space vehicles computer vision Navigation computer architecture Transformers Extraterrestrial measurements Data models

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Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text recognition

Low-Rank Adaptation vs. Fine-Tuning for Handwritten Text Rec...

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ieee Winter Applications and computer vision workshops (WACVW)

作者： Lukas Hüttner Martin Mayr Thomas Gorges Fei Wu Mathias Seuret Andreas Maier Vincent Christlein Pattern Recognition Lab Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The continuous expansion of neural network sizes is a notable trend in machine learning, with transformer models exceeding 20 billion parameters in computer vision. This growth comes with rising demands for computational resources and large-scale datasets. Efficient techniques for transfer learning thus become an attractive option in setups with limited data, as in handwriting recognition. Recently, parameter-efficient fine-tuning (PEFT) methods, such as low-rank adaptation (LoRA) and weight-decomposed low-rank adaptation (DoRA), have gained wide-spread interest. In this paper, we explore tradeoffs in parameter-efficient transfer learning using the synthetically pretrained Transformer-Based Optical Character recognition (TrOCR) model for handwritten text recognition with LoRA and DoRA. Additionally, we analyze the performance of full fine-tuning with a limited number of samples, scaling from a few-shot learning scenario up to using the whole dataset. We conduct experiments on the popular IAM Handwriting database as well as the historical READ 2016 dataset. We find that (a) LoRA/DoRA does not outperform full fine-tuning as opposed to a recent paper and (b) LoRA/DoRA is not substantially faster than full fine-tuning of TrOCR.

关键词： Training Handwriting recognition computer vision Adaptation models Runtime Text recognition Databases Transfer learning Training data Transformers

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Divide and Conquer Boosting for Enhanced Traffic Safety Description and Analysis with Large vision Language Model

Divide and Conquer Boosting for Enhanced Traffic Safety Desc...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Khai Trinh Xuan Khoi Nguyen Nguyen Bach Hoang Ngo Vu Dinh Xuan Minh-Hung An Quang-Vinh Dinh Ho Chi Minh City University of Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City University of Science VNU-HCM Vietnam University of Information Technology VNU-HCM Vietnam FPT Telecom Vietnam AI Lab - AI Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing complexity of traffic dynamics has underscored the necessity for advanced traffic safety description and analysis, challenging the efficacy of current methodologies in comprehensively understanding and predicting safety conditions from transportation videos. This paper addresses these challenges by analyzing specific segments crucial for precise traffic safety descriptions. Through this examination, we introduce an innovative preprocessing method named "segment extraction", facilitating the creation of a novel segment-based training dataset. Additionally, we present a practical two-stage training framework specifically tailored for this dataset. This framework is designed to produce accurate descriptions of traffic safety by incorporating the unique attributes of our segment-based training datasets. Leveraging these contributions, our method achieved a notable 2nd rank with a score of 32.8877 in the AI City Challenge Track2 test set: Traffic Safety Description and Analysis 2024. The source code for the proposed approaches is openly accessible at https://***/AIVIETNAMResearch/AI-CIty-2024-Track2

关键词： Training Pedestrians Source coding Urban areas Data preprocessing Transportation Data models

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2023

Multi-modal Aerial View Object Classification Challenge Resu...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Spencer Low Oliver Nina Angel D. Sappa Erik Blasch Nathan Inkawhich Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH Ecuador Computer Vision Center ESPOL Polytechnic University Spain Air Force Research Laboratory Arlington VA Air Force Research Laboratory Rome NY

This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognition (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model *** majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement.

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BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primit...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sainan Liu Shan Lin Jingpei Lu Alexey Supikov Michael Yip Intel Labs UCSD

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated sampling and hash encoding to expedite automatic pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate 10 to 20 × speed improvement compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://***/IntelLabs/baa-ngp.

关键词： Training Graphics Three-dimensional displays Robot vision systems Video sequences Streaming media Cameras

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Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face recognition

Identity-driven Three-Player Generative Adversarial Network ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jan Niklas Kolf Tim Rieber Jurek Elliesen Fadi Boutros Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available 1 .

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Exploring AI-Based Satellite Pose Estimation: from Novel Synthetic Dataset to In-Depth Performance Evaluation

Exploring AI-Based Satellite Pose Estimation: from Novel Syn...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Fabien Gallet Christophe Marabotto Thomas Chambon Institut de Recherche Technologique Saint Exupéry

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision-based pose estimation using deep learning offers a promising cost effective and versatile solution for relative satellite navigation purposes. Using such a solution in closed loop to control spacecraft position is challenging from validation and performance verification viewpoint, because of the complex specification and development process. The validation task entails bridging the gap between the dataset and real-world data. In particular, modelling of Sun power and spectrum, Earth albedo, and atmospheric absence effects, is costly to replicate on ground. This article suggests a novel approach to produce synthetic space scene images. Fine statistical balancing is ensured to train and assess pose estimation solutions. A physically based camera model is used. Synthetic images incorporate realistic light flux, radiometric properties, and texture scatterings. The dataset comprises 120000 images supplemented with masks, distance maps, celestial body positions, and precise camera parameters (dataset publicly available https://***/space_rendezvous/ created in the frame of a project called RAPTOR: Robotic and Artificial intelligence Processing Test On Representative target). An analysis method using a dedicated metric library has been developed to help the assessment of the solution performance and robustness. A deeper comprehension of algorithm behavior through distribution law fitting and outlier identification is then facilitated. Finally, it is shown that implementing Region-of-Interest (RoI) training can drastically increase the performance of the Convolutional Neural Networks (CNNs) for long-range satellite pose estimation tasks.

关键词： Training Space vehicles computer vision Atmospheric modeling Pose estimation Satellite broadcasting Robot vision systems

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COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Laurens E. Hogeweg Rajesh Gangireddy Django Brunink Vincent J. Kalkman Ludo Cornelissen Jacob W. Kamminga Intel Naturalis Biodiversity Center University of Twente

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： Image quality computer vision Image recognition Databases conferences Biological system modeling Media

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RB-Dust - A Reference-based Dataset for vision-based Dust Removal

RB-Dust - A Reference-based Dataset for Vision-based Dust Re...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Peter Buckel Timo Oksanen Thomas Dietmueller Baden-Wuerttemberg Cooperative State University (DHBW) Ravensburg Germany Chair of Agrimechatronics Munich Institute of Robotics and Machine Intelligence (MIRMI) Technical University of Munich Germany

Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture.

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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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