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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是271-280 订阅

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Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction

Manual Evaluation Matters: Reviewing Test Protocols of Dista...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Gao, Tianyu Han, Xu Bai, Yuzhuo Qiu, Keyue Xie, Zhiyu Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Princeton Univ Dept Comp Sci Princeton NJ USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China WeChat AI Tencent Inc Pattern Recognit Ctr Beijing Peoples R China

ISBN: (纸本)9781954085541

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data. However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled data-which, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. this problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. the experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods. We hope that both our manual test sets and observations can help advance future DS-RE research.(1)

关键词： Extraction

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Estimate of the Neural Network Dimension Using Algebraic Topology and Lie theory 25th

Estimate of the Neural Network Dimension Using Algebraic Top...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Melodia, Luciano Lenz, Richard Chair of Computer Science 6 Friedrich-Alexander University Erlangen-Nürnberg Erlangen91058 Germany

ISBN: (纸本)9783030688202

In this paper we present an approach to determine the smallest possible number of neurons in a layer of a neural network in such a way that the topology of the input space can be learned sufficiently well. We introduce a general procedure based on persistent homology to investigate topological invariants of the manifold on which we suspect the data set. We specify the required dimensions precisely, assuming that there is a smooth manifold on or near which the data are located. Furthermore, we require that this space is connected and has a commutative group structure in the mathematical sense. these assumptions allow us to derive a decomposition of the underlying space whose topology is well known. We use the representatives of the k-dimensional homology groups from the persistence landscape to determine an integer dimension for this decomposition. this number is the dimension of the embedding that is capable of capturing the topology of the data manifold. We derive the theory and validate it experimentally on toy data sets. © 2021, Springer Nature Switzerland AG.

关键词： Topology

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FPGA Implementation and Acceleration of Convolutional Neural Networks

FPGA Implementation and Acceleration of Convolutional Neural...

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international conference for Convergence of Technology (I2CT)

作者： Jayanth N Pisharody Pranav KB Ranjitha M Rajeshwari B PES University Bengaluru

In many areas like pattern recognition, object detection, and image classification, Convolutional Neural Networks(CNN) are becoming prevalent. FPGA implementation of a trained CNN model and accelerating the CNN model by parallelizing the operations within layers is proposed in this paper. the evaluation of the proposed model is done using the MNIST digit dataset and is later observed for other datasets as well. the evaluation is done based on the parameters such as accuracy, resource utilization, and speed up a achieved upon parallelization. It is observed that the accuracy obtained on FPGA is the same as the accuracy obtained on the computer and the model achieved maximum speed up of 2.586 on FPGA after parallelization.

关键词： Computational modeling Object detection Computer architecture pattern recognition Convolutional neural networks Resource management Field programmable gate arrays

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Classification of Driver Intentions at Roundabouts 6

Classification of Driver Intentions at Roundabouts

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6th international conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Sackmann, Moritz Bey, Henrik Hofmann, Ulrich thielecke, Joern FAU Erlangen Nurnberg Inst Informat Technol D-91058 Erlangen Germany AUDI AG Predev Automated Driving D-85057 Ingolstadt Germany

ISBN: (纸本)9789897584190

Classification of other drivers' intentions is an important requirement for automated driving. We present two methods to estimate whether a driver leaves a roundabout. the first, like many other approaches to this problem, requires training data of the specific roundabout to extract typical behavior patterns. Afterwards, these patterns are used for classification of other drivers' intentions. the second approach generates typical behavior patterns from a precise map. Consequently, no training data is required and classification can be performed on arbitrary roundabouts as long as a map is available. Experimental evaluation on a real world dataset of 266 trajectories shows that the performance of the map-based approach is comparable to the data-driven approach. the classification result can be used in a later stage for behavior planning of automated vehicles or driver assistance systems.

关键词： Automated Driving Intention Classification Trajectory Analysis pattern recognition

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Specific Motion pattern Detection: State-of-the-Art and Challenges

Specific Motion Pattern Detection: State-of-the-Art and Chal...

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international conference for Convergence of Technology (I2CT)

作者： Abhay Agrawal Mukesh Kumar Rohil Birla Institute of Technology and Science Pilani Pilani India

Detecting motion patterns in video constitutes a field which requires complex computations as processing of moving frame (video) is to be done. the field requires detection of patterns in motion by evaluating each frame pixel by pixel. Since we are mostly interested in detecting only specific patterns, it is possible that patterns that are not specific could be discarded. Applications of motion pattern detection cut across several verticals including surveillance, storage optimization, video compression, tracking of objects and determining the general direction of motion of multiple objects. In the present work, we have explored the state-of-the-art techniques and algorithms for detection of motion patterns, along with the associated challenges.

关键词： Tracking Surveillance Video compression Airports pattern recognition Security Optimization

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ASMMC21: the 6th international Workshop on Affective Social Multimedia Computing 21

ASMMC21: The 6th International Workshop on Affective Social ...

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23rd ACM international conference on Multimodal Interaction, ICMI 2021

作者： Huang, Dongyan Schuller, Björn Tao, Jianhua Xie, Lei Yang, Jie Ubtech Robotics United States Ieee United States National Laboratory of Pattern Recognition China Northwestern Polytechnical University China Iis Nsf United States

ISBN: (纸本)9781450384810

Affective social multimedia computing is an emergent research topic for both affective computing and multimedia research communities. Social multimedia is fundamentally changing how we communicate, interact, and collaborate with other people in our daily lives. Comparing with well-organized broadcast news and professionally made videos such as commercials, TV shows, and movies, social multimedia media computing imposes great challenges to research communities. Social multimedia contains much affective information. Effective extraction of affective information from social multimedia can greatly help social multimedia computing (e.g., processing, index, retrieval, and understanding). Although much progress have been made in traditional multimedia research on multimedia content analysis, indexing, and retrieval based on subjective concepts such as emotion, aesthetics, and preference, affective social multimedia computing is a new research area. the affective social multimedia computing aims to proceed affective information from social multi-media. For massive and heterogeneous social media data, the research requires multidisciplinary understanding of content and perceptual cues from social multimedia. this workshop served as a successful step towards this goal and attracted contributions from different research disciplines on the analysis of affective signals in interaction (multimodal analyses enabling artificial agents in Human-Machine Interaction, social Interaction with artificial agents) and social multimedia (e.g., twitter, wechat, weibo, youtube, facebook, etc). this paper provides a summary of the activities of the workshop and the accepted papers and abstracts. © 2021 Owner/Author.

关键词： Social networking (online)

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Face recognition from image patches using an ensemble of CNN-local mesh pattern networks

Face recognition from image patches using an ensemble of CNN...

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international conference for Convergence of Technology (I2CT)

作者： Renjith thomas Musaliar College of Engineering and Technology Pathanamthitta India

Face recognition is one of the superior methods among the biometric identification system. Demand for face recognition is increased recently because of the availability of data from security and surveillance cameras. this work proposes a face recognition method using an ensemble of convolutional neural network trained on original and the textural feature maps from image patches. the original face image divided into smaller patches and a textural descriptor is applied on them. the textural feature map image of all patches and the original patches are input to two pre-trained ResNet50 networks. Input images are used to fine-tune the networks. the predictions of the two networks are combined by majority voting ensemble technique to get the final labels. the proposed system achieves an accuracy of 98.05%. the system performance is also evaluated using different measures like precision, recall, and F-measure.

关键词： Image recognition Face recognition System performance Surveillance Cameras Security Convolutional neural networks

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Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT Images 1

引用

1st 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with 23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Chen, Haibin Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Perception Vision Medical Technology Guangzhou China

ISBN: (数字)9783030671945

ISBN: (纸本)9783030671938

the automatic segmentation of head and neck (H&N) tumor from FDG-PET and CT images is urgently needed for radiomics. In this paper, we propose a framework to segment H&N tumor automatically by fusing information of PET and CT. In this framework, multiple 3D-Unets are trained one-by-one. the predictions and features of upstream models will be captured as additional information for the next one to further refine the segmentation. Experiments show that iterative refinements can improve the performance. We evaluated our framework on the dataset of HECKTOR2020 (the challenge of head and neck tumor segmentation) and won the 5th place with average DSC, precision and recall of 0.7241, 0.8479, 0.6701 respectively. © 2021, Springer Nature Switzerland AG.

关键词： Computerized tomography

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SURVANT: An Innovative Semantics-Based Surveillance Video Archives Investigation Assistant 25th

SURVANT: An Innovative Semantics-Based Surveillance Video Ar...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Vella, Giuseppe Dimou, Anastasios Gutierrez-Perez, David Toti, Daniele Nicoletti, Tommaso La Mattina, Ernesto Grassi, Francesco Ciapetti, Andrea McElligott, Michael Shahid, Nauman Daras, Petros Engineering Ingegneria Informatica S.p.a Piazzale dell’Agricoltura 24 Rome00144 Italy Centre for Research and Technology Hellas 6th Km Charilaou-Thermi Thermi57001 Greece Demetrix Srl. Via Ugo La Malfa 28a/30 Palermo90146 Italy United Technologies Research Centre Ireland Ltd. Penrose Wharf Business Centre Cork CityT23 XN53 Ireland Faculty of Mathematical Physical and Natural Sciences Catholic University of the Sacred Heart Via Musei 41 Brescia25121 Italy Innovation Engineering S.r.l. Via Napoleone Colajanni 4 Rome00191 Italy

ISBN: (纸本)9783030687861

SURVANT is an innovative video archive investigation system that aims to drastically reduce the time required to examine large amounts of video content. It can collect the videos relevant to a specific case from heterogeneous repositories in a seamless manner. SURVANT employs Deep Learning technologies to extract inter/intra-camera video analytics, including object recognition, inter/intra-camera tracking, and activity detection. the identified entities are semantically indexed enabling search and retrieval of visual characteristics. Semantic reasoning and inference mechanisms based on visual concepts and spatio-temporal metadata allows users to identify hidden correlations and discard outliers. SURVANT offers the user a unified GIS-based search interface to unearth the required information using natural language query expressions and a plethora of filtering options. An intuitive interface with a relaxed learning curve assists the user to create specific queries and receive accurate results using advanced visual analytics tools. GDPR compliant management of personal data collected from surveillance videos is integrated in the system design. © 2021, Springer Nature Switzerland AG.

关键词： Object recognition

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Searching Efficient Models for Human Activity recognition 21

Searching Efficient Models for Human Activity Recognition

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2021 6th international conference on Biomedical Imaging, Signal Processing

作者： Shamisa Kaspour Nikhil Raj Alankrit Mishra Abdulsalam Yassine thiago Eustaquio Alves De Oliveira Department of Computer Science Lakehead University Canada Department of Software Engineering Lakehead University Canada

ISBN: (纸本)9781450385817

Human Activity recognition (HAR) can be measured in various ways in a new era of growing technologies. this paper studies different classification and data processing tasks. this paper proposes using HAR to monitor the elderly while being power-efficient and respecting an individual’s privacy, allowing it to be run on mobile devices like smartphones or smartwatches. Upon reviewing other methods of HAR by sensor data, we realized that they severely lacked in the areas mentioned earlier. Moreover, they either used older classification techniques or made too complex and over-the-top models for the same. We tested a total of nine methods to find the best model/method, from simple support vector machines (SVM) and convolutional neural networks (CNN) to hybrid models. the best results were produced by a simple, fully connected network (multi-layer perceptron) with the data condensed using Fisher’s linear discriminant analysis (FLDA) that gave us 98.6% accuracy. Our final model satisfies both the requirements we had set; it is simplified and produces benchmark results.

关键词： pattern recognition Linear Discriminant Analysis Smartphone Sensor Data Human Activity recognition

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