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共 106 条记录，以下是61-70 订阅

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Efficient Dense-Graph Convolutional Network with Inductive Prior Augmentations for Unsupervised Micro-Gesture recognition 26

Efficient Dense-Graph Convolutional Network with Inductive P...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Shah, Atif Chen, Haoyu Shi, Henglin Zhao, Guoying Univ Oulu Ctr Machine Vis & Signal Anal CMVS Oulu Finland

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Skeleton-based action/gesture recognition has already witnessed excellent progress on processing large-scale, laboratory-based datasets with pre-defined skeleton joint topology. However, it's still an unsolved task when it comes to real-world scenarios with practical limitations such as small-scaled dataset sizes, few-labeled samples, and various skeleton topologies. In this paper, we work on the recognition of micro-gestures, which are subtle body gestures collected in real-world scenarios. Specifically, we utilize contrastive learning to heritage the knowledge from known large-scale datasets for enhancing the learning on fewer samples of micro-gestures. To overcome the gap caused by various domain distributions and structure topologies between the datasets, we compute skeleton representations from augmented sequences via momentum-based efficient and scalable encoders as additional inductive priors. Importantly, we propose an effective dense-graph based unsupervised architecture that resorts to a queue-based dictionary to store positive and negative keys for better contrast with queries to learn substantially efficient and discriminant patterns in the feature space. Together with cross-dataset experimental results show that our model significantly improves the accuracies on two micro-gesture datasets, SMG by 7.4% and iMiGUE by 18.41% advocating its superiority.

关键词： Dictionaries Computer architecture Skeleton Robustness data models Topology pattern recognition

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A Multimodal Framework for Automated Teaching Quality Assessment of One-to-many Online Instruction Videos 26

A Multimodal Framework for Automated Teaching Quality Assess...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Pan, Yueran Wu, Jiaxin Ju, Ran Zhou, Ziang Gu, Jiayue Zeng, Songtian Yuan, Lynn Li, Ming Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Duke Kunshan Univ Ctr Teaching & Learning Kunshan Jiangsu Peoples R China Duke Kunshan Univ Data Sci Res Ctr Kunshan Jiangsu Peoples R China Fumi Hlth & Technol LLC Hunan Fujian Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the post-pandemic era, online courses have been adopted universally. Manually assessing online course teaching quality requires significant time and professional pedagogy experience. To address this problem, we design an evaluation protocol and propose a multimodal machine learning framework1 for automated teaching quality assessment of one-to-many online instruction videos. Our framework evaluates online teaching quality from five aspects, namely Clarity, Classroom interaction, Technical management of online teaching, Empathy, and Time management. Our method includes mid-level behavior feature extraction, high-level interpretable feature extraction, and supervised learning prediction. Our automated multimodal teaching quality assessment system achieves comparable performance to human annotators on our one-to-many online instruction videos. For binary classification, the best average accuracy of five aspects is 0.898. For regression, the best average means square error is 0.527 on a 0-10 scale.

关键词： Teaching Quality Assessment Multimodal Behavior Coding Interptretable Feature Extraction Speaker Diarization Emotion recognition

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Autoencoder Attractors for Uncertainty Estimation 26

Autoencoder Attractors for Uncertainty Estimation

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: the recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. the use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.

关键词： Training Visualization Uncertainty Computational modeling Estimation Training data Safety

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Advances in Intelligent data Analysis VIII 2009

引用

丛书名： Lecture Notes in Computer Science

2009年

作者： Niall M. Adams Céline Robardet Arno Siebes Jean-Francois Boulicaut

ISBN: (数字)9783642039157

ISBN: (纸本)9783642039140

this book constitutes the refereed proceedings of the 8th international conference on Intelligent data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. the 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic data 26

Towards Automatic Parsing of Structured Visual Content throu...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Schoelch, Lukas Steinhaeuser, Jonas Beichter, Maximilian Seibold, Constantin Yang, Kailun Knaeble, Merlin Schwarz, thorsten Maedche, Alexander Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. this, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. this is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.

关键词： Training Measurement Visualization Annotations Supervised learning Static VAr compensators Solids

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Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface 26

Factorization Approach for Sparse Spatio-Temporal Brain-Comp...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Lee, Byeong-Hoo Cho, Jeong-Hyun Kwon, Byoung-Hee Lee, Seong-Whan Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator;Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.

关键词： Training Face recognition Feature extraction Brain modeling Electroencephalography Brain-computer interfaces Adversarial machine learning

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Boosting the Discriminant Power of Naive Bayes 26

Boosting the Discriminant Power of Naive Bayes

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wang, Shihe Ren, Jianfeng Lian, Xiaoyu Bai, Ruibin Jiang, Xudong Univ Nottingham Sch Comp Sci Ningbo Peoples R China Ningbo Beilun Taihe High Sch Ningbo Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. the proposed stack auto-encoder consists of two auto-encoders for different purposes. the first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. the second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. the proposed method is evaluated on a set of machine-learning benchmark datasets. the experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.

关键词： Correlation Codes Benchmark testing Feature extraction Boosting data models Numerical models

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Proceedings of the 8th international Advance Computing conference, IACC 2018

Proceedings of the 8th International Advance Computing Confe...

引用

8th international Advance Computing conference, IACC 2018

ISBN: (纸本)9781538666784

the proceedings contain 60 papers. the topics discussed include: 3D-printed sole with variable density using foot plantar pressure measurements;HUPM: efficient high utility pattern mining algorithm for e-business;Pubworld -A R2rml mapping driven approach to transform relational database data into shareable format;a pattern and polarization reconfigurable antenna for WLAN application;an efficient method for text encryption using elliptic curve cryptography;a machine learning approach to georeferencing;a new paradigm for generation of fuzzy membership function;on the combined use of electromyogram and accelerometer in lower limb motion recognition;comparative analysis of clustering algorithm for facial recognition system;and a new term weight measure for gender and age prediction of the authors by analyzing their written texts.

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Deep Saliency Map Generators for Multispectral Video Classification 26

Deep Saliency Map Generators for Multispectral Video Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Bayer, Jens Muench, David Arens, Michael Fraunhofer Ctr Machine Learning & Fraunhofer IOSB Ettlingen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing networks, saliency map generators are a possible solution. A saliency map gives a visual hint on what is of special importance for the network's decision, can reveal possible dataset biases and give a more profound insight in the decision process of the black box. this paper investigates how 2D saliency map generators need to be adapted for 3D input data, and additionally, how the methods behave when applied not only to ordinary video input but rather multispectral 3D input data. this is exemplarily shown on 3D video input data in human action recognition in the infrared and visual spectrum and evaluated by using the insertion and deletion metrics. the dataset used in this work is the Multispectral Action dataset, where each scene is available in the long-wave infrared as well as the visual spectrum. To be able to draw a more general conclusion, the two investigated networks, 3D-ResNet 18 and Persistent Appearance Network (PAN), follow a different mindset. It could be shown, that the saliency methods can also be applied to 3D input data with remarkable results. the results show that a combined training with both, infrared and RGB 3D input data, lead to more focused saliency maps and outperform a training with only RGB or infrared data.

关键词： Measurement Training Visualization three-dimensional displays TV Neural networks Generators

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. the Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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