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Multi-label Contrastive Focal Loss for Pedestrian Attribute recognition

Multi-label Contrastive Focal Loss for Pedestrian Attribute ...

引用

international conference on pattern recognition

作者： Xiaoqiang Zheng Zhenxia Yu Lin Chen Fan Zhu Shilong Wang Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of computer science Chengdu University Of Information Technology Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Pedestrian Attribute recognition (PAR) has received extensive attention during the past few years. With the advances of deep convolutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of network parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in term of mean accuracy.

关键词： Training Visualization Force Complex networks Feature extraction pattern recognition Convolutional neural networks

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Peregrine Preying pattern-Based Differential Evolution 3rd

Peregrine Preying Pattern-Based Differential Evolution

引用

3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Jain, Sanjay Sharma, Vivek Kumar Kumar, Sandeep Amity University Rajasthan Jaipur India Jagannath University JaipurRajasthan India

ISBN: (纸本)9789811507502

The differential evaluation (DE) algorithm is an evolutionary algorithm. It is a popular metaheuristics that efficiently solved various complex optimization problems. This paper proposed modification in DE, motivated by the flying pattern of peregrine falcon while preying and named as peregrine preying pattern-based DE algorithm (P3DE). In P3DE, the flying pattern of peregrine ameliorate the exploration potential, while preserving the novel exploitation potential of the DE. The competence, precision, robustness and trustworthiness of the projected P3DE algorithm is analyzed while simulating it over 12 composite benchmark functions of diverse characteristics. The results of P3DE compared with DE and artificial bee colony algorithm to prove its competitiveness for selected problems. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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Multi-task learning with deep dual-path network for facial attribute recognition 9

Multi-task learning with deep dual-path network for facial a...

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9th international conference on computing and pattern recognition, ICCPR 2020

作者： Lai, Xinyu Chen, Si Wang, Da-Han Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450387835

Facial attribute recognition is a popular and challenging research topic in computer vision. In the traditional deep learning based attribute recognition methods, the mid-level network features and the differences between attribute groups are not fully explored. To solve the above problem, a deep dual-path network is proposed for facial attribute recognition. In the multi-task learning framework, two sub-networks are employed to respectively extract the features of two attribute groups, i.e., local attributes and global ones, and designed with both different scale images and different depth networks. Furthermore, an adaptive Focal loss penalty scheme is developed to automatically assign weights to handle the class imbalance problem for facial attribute recognition. Experimental results on the challenging CelebA dataset show that the proposed method achieves the better performance than state-of-the-art methods. © 2020 ACM.

关键词： Deep learning

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Recognizing soft Biometric on Pedestrian Using Convolutional Neural Network

Recognizing Soft Biometric on Pedestrian Using Convolutional...

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2020 international conference on Data Science and Its Applications, ICoDSA 2020

作者： Siswoyo, Tri Broto Arifianto, Anditya Ramadhani, Kutniawan Nur Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728182353

Video surveillance is very important in automatic surveillance. It is usually used to monitor any criminal activity and help to find the perpetrator. In the search for individual among pedestrians when the face image can no longer be recognized, then what can be recognized is the soft-biometric or attribute appears on the pedestrian. The attributes can be in the form of clothes or accessories worn by pedestrians. However, recognizing attributes is a more challenging task as the number of variations of the attribute is increasing. Therefore, there is a need of a system that can effectively recognize the soft-biometric of pedestrians that are expected to help in video surveillance. In this study, we propose a pedestrian soft-biometric recognition system that was built using the Convolutional Neural Network (CNN) method so that it can search for pedestrians wearing various attributes. In recognizing pedestrian soft-biometric, the authors conducted a study using transfer learning from the Resnet50 architecture. As this case belongs to the multi-label classification case, we also observed several scenarios for determining the threshold to better classify the appearance of attributes. Our system able to recognize 35 different attributes with the best results of this research indicate the average accuracy of 90.82% with 80.84% of precision and 81.08% of recall using test data. © 2020 IEEE.

关键词： Computer vision

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Graph Structure Learning Boosted Neural Network for Image Segmentation

Graph Structure Learning Boosted Neural Network for Image Se...

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IEEE conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Jinde Liu Zhang Zhang 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

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

Although Convolutional Neural Networks have made significant progress in image segmentation, it remains inadequate for exploring the structural relationships between image components and how graphs can be employed to guide image segmentation. To explore the structural relationships inherent in image components, the Graph Structure Learning Boosted Neural Network was proposed, which takes the contextual information generated by the CNN as features of the nodes and then uses a self-supervised graph generator to generate an adjacency matrix representing the image components connectivity. Then a Graph Neural Network (GNN) uses the adjacency matrix to fuse information between components according to their connectivity, thus transforming the CNN’s pixel classification problem into the GNN’s pixel classification problem. The whole model is lightweight and scalable, and extensive experiments have demonstrated the scalability of the model alongside the effectiveness of the method.

关键词： Image segmentation Fuses Scalability Surveillance Graph neural networks Generators Convolutional neural networks

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Digital Image Forensics Using Local Optimal-Oriented pattern and ELM 3rd

Digital Image Forensics Using Local Optimal-Oriented Pattern...

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3rd international conference on soft computing: Theories and Applications, SoCTA 2018

作者： Sharma, Surbhi Ghanekar, Umesh Kurukshetra136119 India

ISBN: (纸本)9789811507502

Due to the advances in computer graphic technology, it has become very difficult to classify computer graphics from photographic images. It is also very difficult to distinguish tampered images from authentic images. In the chapter, an algorithm has been proposed to solve both the problems simultaneously. A texture descriptor local optimal-oriented pattern has been used to extract features from computer-generated and photographic images. The extreme learning machine has been used to classify the images. Experiment results demonstrate that it has high accuracy and is also robust against post-processing operations. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer graphics

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Research of Control Chart pattern recognition Based on Wavelet Decomposition in Lithium Battery Production 20

Research of Control Chart Pattern Recognition Based on Wavel...

引用

5th international conference on Big Data and computing, ICBDC 2020

作者： Ning, Zhou Xiaowei, Chen Jianxin, Zhou School of Information Engineering Wuhan University of Technology Wuhan Hubei Province China

ISBN: (纸本)9781450375474

Although the appearance of the control chart can help people visually observe the quality variation in the production process of lithium batteries, the impact of the variation on the battery quality cannot be directly known. How to use the data mining method to find out the relationship between the way of quality parameter variation and the final quality in the production process will have great practical significance for the production of lithium batteries and even the entire industrial production. This paper uses one-dimensional discrete wavelet decomposition (DWT) combined with radial basis function (RBF) neural networks to identify patterns of process parameters' variation in a lithium battery production line, and then uses clustering method combined with mathematical statistics to find factors affecting battery quality. The research results provide theoretical models and empirical results of pattern recognition analysis for the production process of lithium batteries, and provide theoretical basis and analysis methods for other industrial production processes. © 2020 ACM.

关键词： Cluster analysis

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Design and Simulation of Horn Fed Parabolic Reflector Antenna

Design and Simulation of Horn Fed Parabolic Reflector Antenn...

引用

international conference on Intelligent computing and Control Systems (ICICCS)

作者： Shekhar, Ch N. P. V. Chandra Ikshwak, V. Ram Nikhil Murthy, K. N. Ganendra Deepthi, K. V. B. L. Osmania Univ Maturi Venkata Subba Rao Engn Coll Hyderabad India

ISBN: (数字)9781728148762

ISBN: (纸本)9781728148762

In this paper, our main aim is to do the simulation of a metallic horn antenna fed into a metallic parabolic reflector and a comparison of various factors like gain, directivity, radiation efficiency, radiation intensity based on efficiency factor. The simulation of this horn fed parabolic reflector antenna is carried out in ANSYS HFSS 13.0. Results like reflection coefficient, radiation pattern, 3D polar plot, sidelobe level, beamwidth, radiated power, accepted power have been obtained using HFSS 13.0. The scope of the proposed innovation extends within parabolic reflectors with input given by horn antenna. The applications of the designed antenna have been discussed in the future scope. The main advantages have also been discussed.

关键词： parabolic reflector horn gain beamwidth soft HFSS HFSS -high-frequency structural simulator

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Unsupervised 3D Human Pose Estimation in Multi-view-multi-pose Video

Unsupervised 3D Human Pose Estimation in Multi-view-multi-po...

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international conference on pattern recognition

作者： Cheng Sun Diego Thomas Hiroshi Kawasaki Kyushu University

3D human pose estimation from a single 2D video is an extremely difficult task because computing 3D geometry from 2D images is an ill-posed problem. Recent popular solutions adopt fully-supervised learning strategy, which requires to train a deep network on a large-scale ground truth dataset of 3D poses and 2D images. However, such a large-scale dataset with natural images does not exist, which limits the usability of existing methods. While building a complete 3D dataset is tedious and expensive, abundant 2D in-the-wild data is already publicly available. As a consequence, there is a growing interest in the computer vision community to design efficient techniques that use the unsupervised learning strategy, which does not require any ground truth 3D data. Such methods can be trained with only natural 2D images of humans. In this paper we propose an unsupervised method for estimating 3D human pose in videos. The standard approach for unsupervised learning is to use the Generative Adversarial Network (GAN) framework. To improve the performance of 3D human pose estimation in videos, we propose a new GAN network that enforces body consistency over frames in a video. We evaluate the efficiency of our proposed method on a public 3D human body dataset.

关键词： Geometry Computer vision Three-dimensional displays Pose estimation Generative adversarial networks pattern recognition Usability

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Let's Play Music: Audio-Driven Performance Video Generation

Let's Play Music: Audio-Driven Performance Video Generation

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international conference on pattern recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

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