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Self and Channel Attention Network for Person Re-Identification

Self and Channel Attention Network for Person Re-Identificat...

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international conference on pattern recognition

作者： Asad Munir Niki Martinel Christian Micheloni University of Udine Udine Italy

Recent research has shown promising results for person re-identification by focusing on several trends. One is designing efficient metric learning loss functions such as triplet loss family to learn the most discriminative representations. The other is learning local features by designing part based architectures to form an informative descriptor from semantically coherent parts. Some efforts adjust distant outliers to their most similar positions by using soft attention and learn the relationship between distant similar features. However, only a few prior efforts focus on channel-wise dependencies and learn non-local sharp similar part features directly for the degraded data in the person re-identification task. In this paper, we propose a novel Self and Channel Attention Network (SCAN) to model long-range dependencies between channels and feature maps. We add multiple classifiers to learn discriminative global features by using classification loss. Self Attention (SA) module and Channel Attention (CA) module are introduced to model non-local and channel-wise dependencies in the learned features. Spectral normalization is applied to the whole network to stabilize the training process. Experimental results on the person re-identification benchmarks show the proposed components achieve significant improvement with respect to the baseline.

关键词： Training Measurement Correlation Focusing Benchmark testing Market research pattern recognition

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Intelligent Automation & soft computing

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Intelligent Automation & soft computing

作者： Yingtao Jiang

Intelligent Automation & soft computing： An international Journal seeks to provide a common forum for the dissemination of accurate results about the world of artificial intelligence, intelligent automation, control, computer science, modeling and systems engineering. It is intended that the articles published in the journal will encompass both the short and the long term effects of soft computing and other related fields such as robotics, control, computer, cyber security, vision, speech recognition, pattern recognition, data mining, big data, data analytics, machine intelligence and deep learning. It further hopes it will address the existing and emerging relationships between automation, systems engineering, system of computer engineering and soft computing. Intelligent Automation & soft computing is published monthly by Tech Science Press.

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Landmark-RxR: solving vision-and-language navigation with fine-grained alignment supervision 21

Landmark-RxR: solving vision-and-language navigation with fi...

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Proceedings of the 35th international conference on Neural Information Processing Systems

作者： Keji He Yan Huang Qi Wu Jianhua Yang Dong An Shuanglin Sima Liang Wang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences School of Computer Science University of Adelaide School of Artificial Intelligence Beijing University of Posts and Telecommunications Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and School of Future Technology University of Chinese Academy of Sciences Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) and Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

ISBN: (纸本)9781713845393

In Vision-and-Language Navigation (VLN) task, an agent is asked to navigate inside 3D indoor environments following given instructions. Cross-modal alignment is one of the most critical challenges in VLN because the predicted trajectory needs to match the given instruction accurately. In this paper, we address the cross-modal alignment challenge from the perspective of fine-grain. Firstly, to alleviate weak cross-modal alignment supervision from coarse-grained data, we introduce a human-annotated fine-grained VLN dataset, namely Landmark-RxR. Secondly, to further enhance local cross-modal alignment under fine-grained supervision, we investigate the focal-oriented rewards with soft and hard forms, by focusing on the critical points sampled from fine-grained Landmark-RxR. Moreover, to fully evaluate the navigation process, we also propose a re-initialization mechanism that makes metrics insensitive to difficult points, which can cause the agent to deviate from the correct trajectories. Experimental results show that our agent has superior navigation performance on Landmark-RxR, en-RxR and R2R.

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A Survey of Person Re-identification Based on Deep Learning 2021

A Survey of Person Re-identification Based on Deep Learning

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2021 10th international conference on computing and pattern recognition

作者： Zimin Tian Si Chen Da-Han Wang Junwen Lu School of Computer and Information Engineering Xiamen University of Technology China and Fujian Key Laboratory of Pattern Recognition and Image Understanding China School of Computer and Information Engineering Xiamen University of Technology China

ISBN: (纸本)9781450390439

Person re-identification (Re-ID) has been a popular research topic in computer vision in recent years, and it has important application value in numerous fields, such as intelligent security. The person Re-ID task is to identify whether the pedestrians appearing under different cameras are the same person. The traditional person Re-ID methods mainly rely on the characteristics of manual design, and it has difficulty in solving the problems of person occlusion, posture change, and illumination variation. With the wide application of deep learning, the person Re-ID based on deep learning has brought new ideas for solving these problems, and has been widely concerned by scholars. This paper summarizes and analyzes the latest research trends of person Re-ID based on deep learning. In our work, the recent research works of person Re-ID are coarsely categorized into the supervised learning methods and the unsupervised learning methods according to whether the pedestrian images in the training set have real labels. We then describe the representative datasets used in the person Re-ID task. Finally, we conclude and discuss the future directions of the person Re-ID based on deep learning.

关键词： Person re-identification Supervised learning Unsupervised learning Deep learning

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4th international conference on soft computing and Data Mining, SCDM 2020

4th International Conference on Soft Computing and Data Mini...

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4th international conference on soft computing and Data Mining, SCDM 2020

ISBN: (纸本)9783030360559

The proceedings contain 44 papers. The special focus in this conference is on soft computing and Data Mining. The topics include: A Mechanism to Support Agile Frameworks Enhancing Reliability Assessment for SCS Development: A Case Study of Medical Surgery Departments;Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique;Designing Deep Neural Network with Chicken Swarm Optimization for Violence Video Classification Using VSD2014 Dataset;A Comparison of Weighted Support Vector Machine (WSVM), One-Step WSVM (OWSVM) and Iteratively WSVM (IWSVM) for Mislabeled Data;economic Load Dispatch Problem via Simulated Annealing Method;mathematical Modeling of Multimodal Transportation Risks;a Hybrid Social Mining Approach for Companies Current Reputation Analysis;Incorporating the Markov Chain Model in WBSN for Improving Patients’ Remote Monitoring Systems;enhanced Bat Algorithm for Solving Non-Convex Economic Dispatch Problem;reducing the Effects of Time Cheating on the Performance of Divisible Load Scheduling Using Analytical Hierarchy Process;Lungs Cancer Nodules Detection from CT Scan Images with Convolutional Neural Networks;implementing Virtual Machine: A Performance Evaluation;survey of Offline Arabic Handwriting Word recognition;a Comparative Analysis of Open Government Data in Several Countries: The Practices and Problems;a Fuzzy Logic Technique for Optimizing Follicular Units Measurement of Hair Transplantation;residual Neural Network Vs Local Binary Convolutional Neural Networks for Bilingual Handwritten Digit recognition;source Camera Identification for Online Social Network Images Using Texture Feature;Mental Health App Reviews Analyzer (MHARA) Using Logistic Regression and Tri-Gram;A Review on Smart IOT Based Parking System;classification of Metamorphic Virus Using N-Grams Signatures.

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Fusion of Two Novel Local Descriptors for Face recognition in Distinct Challenges 1

Fusion of Two Novel Local Descriptors for Face Recognition i...

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international conference on Smart Technologies and Systems for Next Generation computing (ICSTSN)

作者： Karanwal, Shekhar Graph Era Univ Deemed CSE Dept Dehra Dun 248002 Uttarakhand India

ISBN: (纸本)9781665421119;9781665421126

This work suggests the 2 LBP variants so-called Neighborhood Difference LBP (ND-LBP) and Neighborhood Mean LBP (NM-LBP). In ND-LBP, the comparison is conducted among the neighbor pixels line up along the clockwise direction and in NM-LBP, the neighbors are compared with its mean. The launched descriptors utilizes the new concept as compared to LBP. In LBP, the surrounding neighbors are compared to center pixel. The histograms build from ND-LBP and NM-LBP are amalgamated for constructing the size of discriminant face descriptor so-called ND-LBP+NM-LBP. PCA is processed next for the size compaction and the 2 classifiers owned for matching are SVMs and NN. The datasets on which evaluation is carried out are ORL and GT. The ND-LBP+NM-LBP descriptor brings off splendid results than many other descriptors in MATLAB R2018a. On ORL and GT, the maximum rates attained are 96% and 86.33% by using the RBF (SVM) classifier.

关键词： Local Binary pattern (LBP) Neighborhood Difference LBP (ND-LBP) Neighborhood Mean LBP (NM-LBP) ND-LBP plus NM-LBP Gray Scale Images

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A Comparative Framework for Blocking Artifacts Removal of compressed Images using Fuzzy Logic

A Comparative Framework for Blocking Artifacts Removal of co...

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international conference on computing Sciences (ICCS)

作者： Manu Prakram Amanpreet Singh Jagroop Singh IKGPTU Jalandhar India DAVIET Jalandhar India

ISBN: (纸本)9781665494465

Restoration of an image with blocking artifacts caused by low-bit-rate compression is a difficult task, and blocking artifact assessment techniques play an essential role in the computer vision area. An artifacts removal approach is a critical step in improving the image processing area's dependability and security, allowing for improved understanding in a variety of applications such as pattern recognition, object categorization, surveillance systems. Removal of artifacts is a processing technique that is utilized to give improved picture Quality, and several artifacts removal procedures have previously been used by researchers in the image processing era for this goal. However, when a collection of artifacts is present in an image such as line artifacts, motion artifacts etc. they do not yield acceptable outcomes. We have suggested a comparative methodology for removing line and motion artifacts from digital images utilizing fuzzy logic in this study. This study's key contribution is the creation of a novel fuzzy logic-based hybrid artifacts removal system that improves blocking artifacts efficiency. The suggested framework has its own influence on quality parameters to remove artifact from a picture.

关键词： Fuzzy logic Computer vision Image coding Surveillance Digital images pattern recognition Image restoration

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Conscious Machines for Autonomous Agents and Cybersecurity

Conscious Machines for Autonomous Agents and Cybersecurity

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IEEE international conference on Rebooting computing (ICRC)

作者： Alan M. Kadin Princeton Junction NJUSA

Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.

关键词： Solid modeling Neural networks Virtual environments Predictive models Real-time systems pattern recognition Computer security

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EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial pattern

EEG-Based Cognitive State Assessment Using Deep Ensemble Mod...

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international conference on pattern recognition

作者： Debashis Das Chakladar Shubhashis Dey Partha Pratim Roy Masakazu Iwamura Indian Institute of Technology Roorkee India Institute of Engineering and Management Kolkata India Osaka Prefecture University Japan

ISBN: (纸本)9781728188089;9781728188096

Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. This proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.

关键词： Solid modeling Computational modeling Filter banks Predictive models Brain modeling Feature extraction Electroencephalography

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SCA Net: Sparse Channel Attention Module for Action recognition

SCA Net: Sparse Channel Attention Module for Action Recognit...

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international conference on pattern recognition

作者： Hang Song YongHong Song YuanLin Zhang College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which further increase the computational burden of 3D CNNs. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much fewer parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNext backbone networks. The experimental results also indicate that the resulting network, namely, SCA Net can achieve outstanding performance on UCF-101 and HMDB-51 datasets.

关键词： Three-dimensional displays Correlation Convolution Performance gain Complexity theory pattern recognition Convolutional neural networks

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