ICPR Workshops最新文献

Multi-Channel Time-Series Person and Soft-Biometric Identification 多通道时间序列人和软生物识别

ICPR Workshops Pub Date : 2023-04-04 DOI: 10.48550/arXiv.2304.01585

Nilah Ravi Nair, Fernando Moya Rueda, Christopher Reining, Gernot A. Fink

{"title":"Multi-Channel Time-Series Person and Soft-Biometric Identification","authors":"Nilah Ravi Nair, Fernando Moya Rueda, Christopher Reining, Gernot A. Fink","doi":"10.48550/arXiv.2304.01585","DOIUrl":"https://doi.org/10.48550/arXiv.2304.01585","url":null,"abstract":"Multi-channel time-series datasets are popular in the context of human activity recognition (HAR). On-body device (OBD) recordings of human movements are often preferred for HAR applications not only for their reliability but as an approach for identity protection, e.g., in industrial settings. Contradictory, the gait activity is a biometric, as the cyclic movement is distinctive and collectable. In addition, the gait cycle has proven to contain soft-biometric information of human groups, such as age and height. Though general human movements have not been considered a biometric, they might contain identity information. This work investigates person and soft-biometrics identification from OBD recordings of humans performing different activities using deep architectures. Furthermore, we propose the use of attribute representation for soft-biometric identification. We evaluate the method on four datasets of multi-channel time-series HAR, measuring the performance of a person and soft-biometrics identification and its relation concerning performed activities. We find that person identification is not limited to gait activity. The impact of activities on the identification performance was found to be training and dataset specific. Soft-biometric based attribute representation shows promising results and emphasis the necessity of larger datasets.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125125816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey 海洋巨型动物调查中数据驱动的眩光分类与预测

ICPR Workshops Pub Date : 2023-03-03 DOI: 10.48550/arXiv.2303.12730

J. Power, Derek Jacoby, M. Drouin, Guillaume Durand, Y. Coady, Julian Meng

{"title":"Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey","authors":"J. Power, Derek Jacoby, M. Drouin, Guillaume Durand, Y. Coady, Julian Meng","doi":"10.48550/arXiv.2303.12730","DOIUrl":"https://doi.org/10.48550/arXiv.2303.12730","url":null,"abstract":"Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. The large dataset is labelled with a Cascading Random Forest Model using a na\"ive pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. The pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning; a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133426194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spatial Layout Consistency for 3D Semantic Segmentation 三维语义分割的空间布局一致性

ICPR Workshops Pub Date : 2023-03-02 DOI: 10.48550/arXiv.2303.00939

M. Jameela, G. Sohn

{"title":"Spatial Layout Consistency for 3D Semantic Segmentation","authors":"M. Jameela, G. Sohn","doi":"10.48550/arXiv.2303.00939","DOIUrl":"https://doi.org/10.48550/arXiv.2303.00939","url":null,"abstract":"Due to the aged nature of much of the utility network infrastructure, developing a robust and trustworthy computer vision system capable of inspecting it with minimal human intervention has attracted considerable research attention. The airborne laser terrain mapping (ALTM) system quickly becomes the central data collection system among the numerous available sensors. Its ability to penetrate foliage with high-powered energy provides wide coverage and achieves survey-grade ranging accuracy. However, the post-data acquisition process for classifying the ALTM's dense and irregular point clouds is a critical bottleneck that must be addressed to improve efficiency and accuracy. We introduce a novel deep convolutional neural network (DCNN) technique for achieving voxel-based semantic segmentation of the ALTM's point clouds. The suggested deep learning method, Semantic Utility Network (SUNet) is a multi-dimensional and multi-resolution network. SUNet combines two networks: one classifies point clouds at multi-resolution with object categories in three dimensions and another predicts two-dimensional regional labels distinguishing corridor regions from non-corridors. A significant innovation of the SUNet is that it imposes spatial layout consistency on the outcomes of voxel-based and regional segmentation results. The proposed multi-dimensional DCNN combines hierarchical context for spatial layout embedding with a coarse-to-fine strategy. We conducted a comprehensive ablation study to test SUNet's performance using 67 km x 67 km of utility corridor data at a density of 5pp/m2. Our experiments demonstrated that SUNet's spatial layout consistency and a multi-resolution feature aggregation could significantly improve performance, outperforming the SOTA baseline network and achieving a good F1 score for pylon 89%, ground 99%, vegetation 99% and powerline 98% classes.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition 面向平板和纸张领域适应的表示学习支持在线手写识别

ICPR Workshops Pub Date : 2023-01-16 DOI: 10.48550/arXiv.2301.06293

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

{"title":"Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition","authors":"Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler","doi":"10.48550/arXiv.2301.06293","DOIUrl":"https://doi.org/10.48550/arXiv.2301.06293","url":null,"abstract":"The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation. Such a domain shift can appear in handwriting recognition (HWR) applications where the motion pattern of the hand and with that the motion pattern of the pen is different for writing on paper and on tablet. This becomes visible in the sensor data for online handwriting (OnHW) from pens with integrated inertial measurement units. This paper proposes a supervised DA approach to enhance learning for OnHW recognition between tablet and paper data. Our method exploits loss functions such as maximum mean discrepancy and correlation alignment to learn a domain-invariant feature representation (i.e., similar covariances between tablet and paper features). We use a triplet loss that takes negative samples of the auxiliary domain (i.e., paper samples) to increase the amount of samples of the tablet dataset. We conduct an evaluation on novel sequence-based OnHW datasets (i.e., words) and show an improvement on the paper domain with an early fusion strategy by using pairwise learning.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise 向非专家解释分类:当人们缺乏专业知识时对分类器的事后解释的XAI用户研究

ICPR Workshops Pub Date : 2022-12-19 DOI: 10.48550/arXiv.2212.09342

Courtney Ford, Markt. Keane

引用次数: 3

A Masked Face Classification Benchmark on Low-Resolution Surveillance Images 低分辨率监控图像的蒙面分类基准

ICPR Workshops Pub Date : 2022-11-23 DOI: 10.1007/978-3-031-37660-3_4

Federico Cunico, Andrea Toaiari, M. Cristani

引用次数: 1

Motif-guided Time Series Counterfactual Explanations 主题引导的时间序列反事实解释

ICPR Workshops Pub Date : 2022-11-08 DOI: 10.48550/arXiv.2211.04411

Peiyu Li, S. F. Boubrahimi, S. M. Hamdi

{"title":"Motif-guided Time Series Counterfactual Explanations","authors":"Peiyu Li, S. F. Boubrahimi, S. M. Hamdi","doi":"10.48550/arXiv.2211.04411","DOIUrl":"https://doi.org/10.48550/arXiv.2211.04411","url":null,"abstract":"With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. The XAI paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. While feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. In this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes. To the best of our knowledge, this is the first effort that leverages motifs to guide the counterfactual explanation generation. We validated our model using five real-world time-series datasets from the UCR repository. Our experimental results show the superiority of MG-CF in balancing all the desirable counterfactual explanations properties in comparison with other competing state-of-the-art baselines.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115486619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection 了解分布外检测的对比学习的性质和局限性

ICPR Workshops Pub Date : 2022-11-06 DOI: 10.48550/arXiv.2211.03183

Nawid Keshtmand, Raúl Santos-Rodríguez, J. Lawry

{"title":"Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection","authors":"Nawid Keshtmand, Raúl Santos-Rodríguez, J. Lawry","doi":"10.48550/arXiv.2211.03183","DOIUrl":"https://doi.org/10.48550/arXiv.2211.03183","url":null,"abstract":"A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants of contrastive learning, namely instance and class discrimination, targeting features that can discriminate between different instances for the former, and different classes for the latter. In this paper, we aim to understand the effectiveness and limitation of existing contrastive learning methods for OOD detection. We approach this in 3 ways. First, we systematically study the performance difference between the instance discrimination and supervised contrastive learning variants in different OOD detection settings. Second, we study which in-distribution (ID) classes OOD data tend to be classified into. Finally, we study the spectral decay property of the different contrastive learning approaches and examine how it correlates with OOD detection performance. In scenarios where the ID and OOD datasets are sufficiently different from one another, we see that instance discrimination, in the absence of fine-tuning, is competitive with supervised approaches in OOD detection. We see that OOD samples tend to be classified into classes that have a distribution similar to the distribution of the entire dataset. Furthermore, we show that contrastive learning learns a feature space that contains singular vectors containing several directions with a high variance which can be detrimental or beneficial to OOD detection depending on the inference approach used.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125147342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Smart Doors: A Position Paper 走向智能门:一份意见书

ICPR Workshops Pub Date : 2022-09-23 DOI: 10.48550/arXiv.2209.11770

Luigi Capogrosso, Geri Skenderi, Federico Girella, F. Fummi, M. Cristani

引用次数: 3

Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss 基于软标签和噪声鲁棒性损失的弱监督医学图像分割

ICPR Workshops Pub Date : 2022-09-16 DOI: 10.48550/arXiv.2209.08172

B. Felfeliyan, A. Hareendranathan, G. Kuntze, S. Wichuk, N. Forkert, J. Jaremko, J. Ronsky

{"title":"Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss","authors":"B. Felfeliyan, A. Hareendranathan, G. Kuntze, S. Wichuk, N. Forkert, J. Jaremko, J. Ronsky","doi":"10.48550/arXiv.2209.08172","DOIUrl":"https://doi.org/10.48550/arXiv.2209.08172","url":null,"abstract":"Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a\"noise-tolerant loss\"function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels.","PeriodicalId":391161,"journal":{"name":"ICPR Workshops","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117022881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0