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Dynamic Guided Network for Monocular Depth Estimation

Dynamic Guided Network for Monocular Depth Estimation

引用

international conference on pattern recognition

作者： Xiaoxia Xing Yinghao Cai Yanqing Wang Tao Lu Yiping Yang Dayong Wen University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

Self-attention and encoder-decoder have been widely used in the deep neural network for monocular depth estimation. The self-attention mechanism is capable of capturing long-range dependencies by computing the representation of each image position by a weighted sum of the features at all positions, while the encoder-decoder can capture detailed structural information by gradually recovering spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet [1] by adding an effective decoder module to progressively refine the coarse depth map. In the decoder stage, we design a dynamic guided upsampling module that employs dynamically generated kernel conditioned on low-level features to guide the upsampling of the coarse depth map. Experimental results demonstrate that our method obtains higher accuracy and generates visually pleasant depth maps.

关键词： Computational modeling Neural networks Estimation Decoding pattern recognition Kernel

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Proceedings - 2020 international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2020

Proceedings - 2020 International Conference on Intelligent C...

引用

2020 international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2020

ISBN: (纸本)9781665423168

The proceedings contain 87 papers. The topics discussed include: U.S. pandemic prediction using regression and neural network models;vertical oil-in-water flow pattern identification with deep CNN-LSTM network;hessian-regularized spectral clustering for behavior recognition;research on variable scale algorithm;design and implementation of discipline competition management system;research on wearable lower limbs control system based on LabView;research progress of face recognition based on deep learning;unmanned aerial vehicle classification and detection based on deep transfer learning;path planning algorithm for UAV in complex environment based on state change;and design and implementation of e-commerce recommendation system model based on user clustering.

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Atmospheric Blocking pattern recognition in Global Climate Model Simulation Data

Atmospheric Blocking Pattern Recognition in Global Climate M...

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international conference on pattern recognition

作者： Grzegorz Muszynski Prabhat Jan Balewski Karthik Kashinath Michael Wehner Vitaliy Kurlin National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory Berkeley CA USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA USA University of Liverpool Liverpool UK

In this paper, we address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a crucial problem to society and natural infrastructure, as they often lead to weather extremes, such as heat waves, heavy precipitation, and the unusually poor air condition. Moreover, it is very challenging to detect these events as there is no physics-based model of blocking dynamic development that could account for their spatiotemporal characteristics. Here, we propose a new two-stage hierarchical pattern recognition method for detection and localisation of atmospheric blocking events in different regions over the globe. For both the detection stage and localisation stage, we train five different architectures of a convolutional neural network (CNN) based classifier and regressor. The results show the general pattern of the atmospheric blocking detection performance increasing significantly for the deep CNN architectures. In contrast, we see the estimation error of event location decreasing significantly in the localisation problem for the shallow CNN architectures. We demonstrate that CNN architectures tend to achieve the highest accuracy for blocking event detection and the lowest estimation error of event localisation in regions of the Northern Hemisphere than in regions of the Southern Hemisphere.

关键词： Heating systems Estimation error Event detection Atmospheric modeling Data models pattern recognition Spatiotemporal phenomena

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Algebraic Bayesian networks: A frequentist approach to knowledge pattern parameters machine learning 8

Algebraic Bayesian networks: A frequentist approach to knowl...

引用

Russian Advances in Fuzzy Systems and soft computing: Selected Contributions to the 8th international conference on "Fuzzy Systems, soft soft computing and Intelligent Technologies",FSSCIT 2020

作者： Tulupyev, Alexandr Kharitonov, Nikita Saint Petersburg State University University Embankment 7/9 St Petersburg199034 Russia SPIIRAS 14-Ya Liniya V.O. 39 St Petersburg199178 Russia

Algebraic Bayesian networks are related to the class of probabilistic graphical models. As a machine learning model they are required to be trained on some data set. This work is dedicated to the frequentist approach to machine learning of a knowledge pattern as a local learning of the Algebraic Bayesian network. The theoretical explanation of approach is provided and the algorithm is described. The algorithm’s pseudocode is presented, its theoretical complexity is calculated. Then an experiment is conducted and real estimates of the algorithm's implementation time of work are received. © 2020 CEUR-WS. All rights reserved.

关键词： Bayesian networks

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MFI: Multi-range Feature Interchange for Video Action recognition

MFI: Multi-range Feature Interchange for Video Action Recogn...

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international conference on pattern recognition

作者： Sikai Bai Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China

Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

关键词： Three-dimensional displays Convolution Feature extraction Encoding pattern recognition Optical flow Videos

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Focal Model in the pattern recognition Problem 2nd

Focal Model in the Pattern Recognition Problem

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2nd international conference of Artificial Intelligence, Medical Engineering, Education (AIMEE)

作者： Rakcheeva, T. Russian Acad Sci Mech Engn Res Inst 4 Maly Kharitonievskiy Pereulok Moscow 101990 Russia

ISBN: (纸本)9783030120825;9783030120818

The pattern recognition problem in the focal paradigm is considered, which makes it possible to apply the focal model both at the learning stage and at the identification stage. The theoretical substantiation and methods of algorithmic realization of the description of classes and their boundaries with the help of the focal model of representation of smooth curves and surfaces by multifocal lemniscates are given. As a result, at the learning stage for each class in the feature space a multipolar space with a focal distance is formed, in which the class boundary is described by a lemniscate and is an isometric surface. An important advantage is that the focal classification space also forms a continuous affiliation function. At the decision-making stage, the advantage of the focal model is that identification is an elementary operation of calculating the focal distance of a given point and comparing its value with the boundary parameter. Thus, the multidimensional classification problem reduces to optimization and decision-making in one-dimensional space. The complexity of the traditional solution, resulting in a fragmented description of class boundaries, is compensated by a high level of computer power that allow successfully cope with practical classification tasks. At the same time, a person has, in addition to a strong applied rationale, the desire for adequacy and organicity of the approach, the result of which is simplicity and sensibility. Such a solution of the classical recognition problem in the focal paradigm is proposed in this paper.

关键词： pattern recognition Decision-making Focal model Lemniscate Curves Surfaces Affiliation function

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On the Optimization of Embedding Spaces via Information Granulation for pattern recognition

On the Optimization of Embedding Spaces via Information Gran...

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international Joint conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Martino, Alessio Mascioli, Fabio Massimo Frattale Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781728169262

Embedding spaces are one of the mainstream approaches when dealing with structured data. Granular computing, in the last decade, emerged as a powerful paradigm for the automatic synthesis of embedding spaces that, at the same time, yield an interpretable model on the top of meaningful entities known as "information granules". Usually, in these contexts, one aims at finding the smallest set of information granules in order to boost the model interpretability while keeping satisfactory performances. In this paper, we add a third objective, namely the structural complexity of the resulting model and we exploit three biology-related case studies related to metabolic networks and protein networks in order to investigate the link between classification performances, embedding space dimensionality and structural complexity of the resulting model.

关键词： Granular computing Embedding Spaces Support Vector Machine Systems Biology Topological Data Analysis Computational Biology

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Causal Inference and Counterfactual Reasoning 2020

Causal Inference and Counterfactual Reasoning

引用

Joint international conference on 7th ACM IKDD international conference on Data Science (CoDS) and 25th international conference on Management of Data (COMAD)

作者： Sharma, Amit Kiciman, Emre Microsoft Res New York NY 10011 USA

ISBN: (纸本)9781450377386

As computing systems are more frequently and more actively intervening to improve people's work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. Conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal analysis. This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from a broad literature on the topic from statistics, social sciences and machine learning. We will motivate the use of causal inference through examples in domains such as recommender systems, social media datasets, health, education and governance. To tackle such questions, we will introduce the key ingredient that causal analysis depends on-counterfactual reasoning-and describe the two most popular frameworks based on Bayesian graphical models and potential outcomes. Based on this, we will cover methods suitable for doing causal inference with large-scale online data, including randomized experiments, observational methods like matching and stratification, and natural experiment-based methods such as instrumental variables and regression discontinuity. We will also focus on best practices for evaluation and validation of causal inference techniques, drawing from our own experiences. We will show application of these techniques using DoWhy, a Python library for causal inference. Throughout, the emphasis will be on considerations of working with large-scale data, such as logs of user interactions or social data.

关键词： pattern recognition

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Indonesian Ethnicity recognition Based on Face Image Using Uniform Local Binary pattern (ULBP) and Color Histogram 4

Indonesian Ethnicity Recognition Based on Face Image Using U...

引用

4th international conference on Informatics and Computational Sciences, ICICoS 2020

作者： Putri, Tiani Tiara Rachmawati, Ema Sthevanie, Febryanti Telkom University School of Computing Bandung West Java Indonesia

ISBN: (纸本)9781728195261

Ethnicity is one of identity every human has and can be used to categorize individuals in populations or large groups. We presented an Indonesian ethnicity recognition based on facial images using Uniform Local Binary pattern (ULBP) and Color Histogram as a feature extraction method. We used the five largest ethnic groups in Indonesia, namely Sundanese, Javanese, Banjar, Buginese, and Malay. In the experiment, we used Random Forest as a classification method. The research obtained a performance accuracy of 98.25% using 2290 facial images. © 2020 IEEE.

关键词： Decision trees

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Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

Enhancement of traditional clothes pattern recognation using...

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computing (ICOCO), IEEE international conference on

作者： Abdul Haris Rangkuti Varyl Hasbi Athala Naufal Fauzi Luthfi Syaugi Vikri Aditama Muhammad Maulana Ramadhan Ayu Hidayah Aslamia School of Computer Scince Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665436908

In general, Traditional clothes in Indonesia has its own characteristics to recognize the origin of the clothes. The formation of traditional clothes patterns in Indonesia from the crafts of remote communities in the regions of Indonesia. The article is that Indonesian citizens still find it difficult to distinguish traditional clothes that are scattered throughout Indonesia. To support this research, we use the Convolutional neural network method for types VGG16, VGG19 and MobileNetV2. To support the recognition process for a variety of traditional clothes patterns, the pattern processing process has started from preprocessing and image segmentation. This research was conducted on 5 types of experiments with supporting parameters, namely the number of training images and test images, rotation and scale of each image, image class and CNN parameters. After we conducted experiments on a collection of images that could be found in various scales and degrees, the average classification accuracy using VGG 16 was 79,23% in condition 2, but the highest classification accuracy was between 84,2%-92,5%. Meanwhile, if using VGG 19 the average classification accuracy is 79,95% with condition 1 for the highest accuracy between 92,6%-96,3%. If using mobilenetV2 the average classification accuracy is 83,44% for the highest accuracy between 87,3%-96,4%. Basically Research for traditional clothes pattern recognition using Deep CNN has succeeded in recognizing some traditional clothes patterns up to more than 92%.

关键词： Training Image segmentation Image recognition conferences Convolutional neural networks Character recognition

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