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DAL: A Deep Depth-Aware Long-term Tracker

DAL: A Deep Depth-Aware Long-term Tracker

引用

international conference on pattern recognition

作者： Yanlin Qian Song Yan Alan Lukežič Matej Kristan Joni-Kristian Kämäräinen Jiří Matas Computing Sciences Tampere University Finland Faculty of Computer and Information Science University of Ljubljana Slovenia Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets. In this work, we propose a deep depth-aware long-term tracker that achieves state-of-the-art RGBD tracking performance and is fast to run. We reformulate deep discriminative correlation filter (DCF) to embed the depth information into deep features. Moreover, the same depth-aware correlation filter is used for target redetection. Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.

关键词： Target tracking Correlation Benchmark testing Information filters pattern recognition Optimization

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Hierarchical Routing Mixture of Experts

Hierarchical Routing Mixture of Experts

引用

international conference on pattern recognition

作者： Wenbo Zhao Yang Gao Shahan Ali Memon Bhiksha Raj Rita Singh Carnegie Mellon University Pittsburgh PA

In regression tasks, the data distribution is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate our method's effectiveness compared to various other regression models.

关键词： Predictive models Routing Probabilistic logic Prediction algorithms Data models Classification algorithms pattern recognition

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Moto: Enhancing Embedding with Multiple Joint Factors for Chinese Text Classification

Moto: Enhancing Embedding with Multiple Joint Factors for Ch...

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international conference on pattern recognition

作者： Xunzhu Tang Rujie Zhu Tiezhu Sun Shi Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China University of Central Florida Orlando FL USA Momenta Suzhou China Institute of Computing Technology Chinese Academy beijing China

Recently, language representation techniques have achieved great performances in text classification. However, most existing representation models are specifically designed for English materials, which may fail in Chinese because of the huge difference between these two languages. Actually, few existing methods for Chinese text classification process texts at a single level. However, as a special kind of hieroglyphics, radicals of Chinese characters are good semantic carriers. In addition, Pinyin codes carry the semantic of tones, and Wubi reflects the stroke structure information, etc. Unfortunately, previous researches neglected to find an effective way to distill the useful parts of these four factors and to fuse them. In our works, we propose a novel model called Moto: Enhancing Embedding with Multiple Joint Factors. Specifically, we design an attention mechanism to distill the useful parts by fusing the four-level information above more effectively. We conduct extensive experiments on four popular tasks. The empirical results show that our Moto achieves SOTA 0.8316 (F₁-score, 2.11% improvement) on Chinese news titles, 96.38 (1.24% improvement) on Fudan Corpus and 0.9633 (3.26% improvement) on THUCNews.

关键词： Fuses Text categorization Semantics pattern recognition Task analysis

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A school bullying detecting algorithm based on motion recognition and speech emotion recognition

A school bullying detecting algorithm based on motion recogn...

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international conference on Intelligent computing and Human-Computer Interaction (ICHCI)

作者： Wei, Chuqiao Zhang, Hua Ye, Liang Meng, Fanchao Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Shandong Peoples R China Harbin Inst Technol Dept Informat & Commun Engn Harbin 150080 Peoples R China

ISBN: (纸本)9781665423168

School bullying is a common social problem among teenagers. It affects the victims both mentally and physically, and is considered as one of the main reasons for depression, dropping out of school, and adolescent suicide. For this reason, preventing school bullying is significant to the student's mental and physical health. In order to detect bullying events in time, this paper proposes a bullying detecting algorithm based on motion recognition and speech emotion recognition. People wear an electronic equipment, which is used to collect his/her motion and speech data, to detect bullying events in real-time. In this paper, the authors extract five features from acceleration and gyro data for physical bullying detection. The PLP features are extracted for verbal bullying detection. Then authors use the Relief-F algorithm for feature selection, and the PPCA algorithm is used to reduce the dimensionality of the feature matrix. Finally, the authors use the KNN algorithm as the classifier to train the motion recognition model and the SVM algorithm as the classifier to train the speech emotion recognition model. With cross-validation, the average accuracy of the motion recognition system is 80.61%, whereas that of the speech emotion recognition system is 75.76%. The simulation results of the algorithm indicate that the anti-bullying detecting algorithm could identify the bullying event effectively.

关键词： activity recognition school bullying artificial intelligence pattern recognition

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VSR++: Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

VSR++: Improving Visual Semantic Reasoning for Fine-Grained ...

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international conference on pattern recognition

作者： Hui Yuan Yan Huang Dongbo Zhang Zerui Chen Wenlong Cheng Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The College of Automation and Electronic Information Xiangtan University Xiangtan China Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

Image-text matching has made great progresses recently, but there still remains challenges in fine-grained matching. To deal with this problem, we propose an Improved Visual Semantic Reasoning model (VSR++), which jointly models 1) global alignment between images and texts and 2) local correspondence between regions and words in a unified framework. To exploit their complementary advantages, we also develop a suitable learning strategy to balance their relative importance. As a result, our model can distinguish image regions and text words in a fine-grained level, and thus achieves the current state-of-the-art performance on two benchmark datasets.

关键词： Visualization Semantics Benchmark testing Cognition pattern matching

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Gyroscope fault pattern recognition based on wavelet packet decomposition and fuzzy clustering

Gyroscope fault pattern recognition based on wavelet packet ...

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2020 international conference on Applied Physics and computing, ICAPC 2020

作者： Liu, Zhaozheng Xiao, Mingqing Zhu, Haizhen Li, Jianfeng College of Aeronautics Engineering Air Force Engineering University Xi'an China

Aiming at the problem that the sensor component's inertial navigation gyro failure symptom is not obvious in a certain aircraft flight control system, this paper proposes a fault cluster recognition method based on fuzzy clustering. First, use wavelet packet decomposition to perform three-layer wavelet packet decomposition on the gyro output data to obtain the feature extraction of the gyro fault signal;then use the fuzzy clustering method to classify the extracted fault signal to achieve gyro fault pattern recognition in the sensor assembly the goal of. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Fuzzy clustering

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Learning Temporal Structure of Videos for Action recognition Using pattern Theory 20

Learning Temporal Structure of Videos for Action Recognition...

引用

6th international conference on computing and Artificial Intelligence, ICCAI 2020

作者： Zhang, Xiaoyu Hefei University of Technology China

ISBN: (纸本)9781450377089

Aiming at the problem that a large amount of background information in the videos cause low judgment of actions, this paper proposed a graph model based on pattern theory for human complex action recognition. Firstly, a video is divided into video units and each video unit corresponds to an atomic action. The atomic action labels of videos are initialized by k-Means. Secondly, the key generator proposal module and the interpretative operation module are proposed to select important foreground information and obtain a reasonable representation of atomic action sequences. In the inference stage, the atomic action sequences of test videos are matched with template sequences by the Dynamic Time Warping algorithm (DTW) to obtain the action categories. The experimental results show that compared with the most existing human action recognition models, our model can explain the temporal process of action occurrence and obtain a more discriminatory sequence representation, which can effectively improve the accuracy of action recognition. © 2020 ACM.

关键词： Atoms

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Identification of Cyclic Changes in the Operation Mode of the Production Facility Based on the Monitoring Data

Identification of Cyclic Changes in the Operation Mode of th...

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international Mechatronics conference

作者： Davydenko, Nina Korobiichuk, Igor Davydenko, Liudmyla Nowicki, Michal Davydenko, Volodymyr Natl Univ Water & Environm Engn Rivne Ukraine Warsaw Univ Technol Warsaw Poland Lutsk Natl Tech Univ Lutsk Ukraine

ISBN: (纸本)9783030299934;9783030299927

The article deals with the issues of formalizing the change of the actual conditions of the operation mode of the production facility. The purpose of the article is to develop a mechanism for identifying these changes based on the analysis of data obtained from the monitoring system the operation mode of the facility. The expediency of using the pattern recognition apparatus for solving the issue is substantiated. As an indicator of changing operation conditions, it is suggested to use the profile of the relevant factor of the external environment described by morphometric indicators. The two-step procedure is proposed based on the successive use of pattern recognition algorithms without and with training. It provides the formation of knowledge about the possible states of the facility operation conditions and the construction of classifier to determine the accordance to one of them. Structurally-parametric identification of the classifier model is performed by the group method of data handling. The use of the proposed mechanism will contribute to the effective planning of operation modes of production facilities, the determination of time ranges for supporting such modes, their control, and the prevention of non-typical operation modes.

关键词： pattern recognition Relevant factor profile Morphometric parameters Group method of data handling Classifier

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

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Algorithms, High Performance computing and Artificial Intelligence (AHPCAI), international conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution Image color analysis Convolution Brightness Lighting Information filters

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Mixture Loss Function-based Classification Network for Few-shot Learning

Mixture Loss Function-based Classification Network for Few-s...

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computing, Robotics and System Sciences (ICRSS), international conference on

作者： Yansha Zhang Feng Pan Jie Wang Lin Wang College of Date Science and Information Engineering GuiZhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guiyang China

Data augmentation technology can effectively solve the problem of few-shot image classification, but many approaches based on data augmentation generated sample have poor discriminability, which negatively affects the classifier and classification accuracy needs to be improved. In this regard, based on the TFFCN, a mixture loss function-based classification network for few-shot learning (MLFCN) is proposed. First of all, for the prototype tensor features with a simple mean representation, ignoring the different contributions of the sample features of support set for per class to the class prototype tensor features, so that the generated prototype tensor features can not well represent sample features of per class, affecting the discriminability of the generated samples. Introduce the improved adaptively spatial feature fusion module, it uses the convolution, considers the support set of different sample features on the prototype tensor, adaptively assign weights, then weighted and summed to obtain a more representative prototype tensor feature; secondly, aiming at the blurred classification boundary of similar categories in the generated sample features, a mixture loss function is proposed. The loss function is to add the inter-class mean squared error term to the mean square error loss, and stretches the inter-class distance of the generated tensor feature, so that the classification boundary is clearer, so as to train a better classifier. Experimental results show that the improved adaptively spatial feature fusion and the mixing loss function can augment the discriminability of the generated tensor features and further improve the classification accuracy.

关键词： Training Tensors Convolution Prototypes Mean square error methods Feature extraction Generators

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