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Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning

Building Computationally Efficient and Well-Generalizing Per...

引用

International conference on pattern recognition

作者： Vladislav Sovrasov Dmitry Sidnev IOTG Computer Vision (ICV) Intel

this work considers the problem of domain shift in person re-identification. Being trained on one dataset, a reidentification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AMSoftmax loss, and some additional training practices to build wellgeneralizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMTI7 dataset in three setups: MSMTI7-all→DukeMTMC, MSMTI7-train→MarketI5OI and MSMTI7-all→MarketI5OI. Training code and the models are available online in the Github repository¹.

关键词： Training Measurement Computational modeling Architecture Face recognition computer architecture Feature extraction

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4D unsupervised object discovery 22

4D unsupervised object discovery

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Proceedings of the 36th International conference on Neural Information Processing Systems

作者： Yuqi Wang Yuntao Chen Zhaoxiang Zhang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HKISI_CAS Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) and School of Artificial Intelligence University of Chinese Academy of Sciences and Centre for Artificial Intelligence and Robotics HKISI_CAS

ISBN: (纸本)9781713871088

Object discovery is a core task in computer vision. While fast progresses have been made in supervised object detection, its unsupervised counterpart remains largely unexplored. With the growth of data volume, the expensive cost of annotations is the major limitation hindering further study. therefore, discovering objects without annotations has great significance. However, this task seems impractical on still-image or point cloud alone due to the lack of discriminative information. Previous studies underlook the crucial temporal information and constraints naturally behind multi-modal inputs. In this paper, we propose 4D unsupervised object discovery, jointly discovering objects from 4D data - 3D point clouds and 2D RGB images with temporal information. We present the first practical approach for this task by proposing a ClusterNet on 3D point clouds, which is jointly it- eratively optimized with a 2D localization network. Extensive experiments on the large-scale Waymo Open Dataset suggest that the localization network and ClusterNet achieve competitive performance on both class-agnostic 2D object detection and 3D instance segmentation, bridging the gap between unsupervised methods and full supervised ones.

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Synchronous System for Driver Drowsiness Detection Using Convolutional Neural Network, computer vision and Android Technology

Synchronous System for Driver Drowsiness Detection Using Con...

引用

International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Purvika Bajaj Renesa Ray Shivani Shedge Sagar Jaikar Pranav More MIT ADT University Pune India

ISBN: (纸本)9781665405218;9781665446341

One of the major reasons behind car accidents is the drowsy nature acquired by a driver while driving any vehicle. Owing to the ongoing scenario, in this project, we aim to develop a real time driver drowsiness detection system in order to detect the drivers' fatigue status, such as dozing, flickering of eye lids and time span of eye closure without having to equip their bodies with devices. the objective of this project is to build a drowsiness detection system that will detect that a person's eyes are closed for a few seconds. this system will alert the driver when drowsiness is detected. this approach is based on the Convolutional Neural Network that can be implemented on Android applications with high accuracy. Apart from CNN, computer vision also plays a major role to detect the drowsiness pattern of the driver. Cloud architecture has also proved to be beneficial in case of capturing and analyzing real time video streams.

关键词： computer vision Cloud computing Communication systems computer architecture Streaming media Fatigue Real-time systems

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Crowd Counting for Static Images: A Survey of Methodology 39

Crowd Counting for Static Images: A Survey of Methodology

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39th chinese Control conference (CCC)

作者： Luo, Ying Lu, Jinhu Zhang, Baochang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China Beihang Univ Shenyuan Honors Coll Beijing 100191 Peoples R China Beiluing Univ State Key Lab Software Dev Environm Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beiluing Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing 100191 Peoples R China

ISBN: (纸本)9789881563903

Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. this paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.

关键词： Crowd counting crowd analysis pattern recognition computer vision

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A Deformable Convolutional Neural Network with Oriented Response for Fine-Grained Visual Classification 2021

A Deformable Convolutional Neural Network with Oriented Resp...

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2021 13th International conference on Machine Learning and Computing, ICMLC 2021

作者： Ruan, Shangxian Yang, Jiating Chen, Jianbo Amazingx Academy Foshan China Xian Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xian China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781450389310

Fine-grained visual classification (FGVC) aims to classify images belonging to the same basic category in a more detailed sub-category. It is a challenging research topic in the field of computer vision and pattern recognition in recent years. the existing FGVC method conduct the task by considering the part detection of the object in the image and its variants, which rarely pays attention to the difference in expression of many changes such as object size, posture, and perspective. As a result, these methods generally face two major difficulties: 1) How to effectively pay attention to the latent semantic region, and reduce the interference caused by many changes in pose and perspective;2) How to extract rich feature information for non-rigid and weak structure objects. In order to solve these two problems, this paper proposes a deformable convolutional neural network with oriented response for FGVC. the proposed method can be divided into three main steps: firstly, the local region of latent semantic information is localized based on a lightweight CAM network;then, the deformable convolutional ResNet-50 network and the rotation-invariant coding oriented response network are designed, which input the original image and local region into the feature network to learn the discriminant features of rotation invariance;finally, the learned features are embed into a joint loss to optimize the entire network end-to-end. Experiments are carried out on three challenging FGVC datasets, including CUB-200-2011, FGVC_Aircraft and Aircraft_2 datasets. the results show that the accuracy of the proposed method on all datasets is better than the comparison method, which can effectively improve the accuracy of weakly supervised FGVC. © 2021 ACM.

关键词： Convolution

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A Real-time Tracking System Used for Surface Detection 7

A Real-time Tracking System Used for Surface Detection

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7th International conference on Information Science and Control Engineering, ICISCE 2020

作者： Wang, Xiao Li, Bo Zhang, Zhankuan Research Institute of Wood Industry Chinese Academy of Forestry Beijing China

ISBN: (纸本)9781728164069

In industrial production, the high speed of object on conveyor always causes motion blur when computer vision is used to achieve appearance inspection, results in difficulties in image processing. In this study, a real-time tracking system based on cam transmission was applied to eliminate the relative movement between camera and objects. the outline of the cam was specifically designed to meet the requirement of real-time tracking and simplified the motor control. To test the system, the velocities of both camera and object were collected using rotary encoders. It was found that the camera velocity would be almost equal to that of object in a certain period when cam rotated uniformly. Also, the images were captured by triggering the camera at the suitable moment, and it was shown that the object in image was very clear. Compared with conventional restoration algorithms, the proposed method resulted in clearer images without any distortion, and could save computation time, which was especially suitable for on-line inspection in industrial production such as boards. © 2020 IEEE.

关键词： computer vision

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Learning to Rank for Active Learning: A Listwise Approach

Learning to Rank for Active Learning: A Listwise Approach

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International conference on pattern recognition

作者： Minghan Li Xialei Liu Joost van de Weijer Bogdan Raducanu Universite Grenoble Alpes LIG Grenoble France Computer Vision Center Universitat Autonoma de Barcelona Barcelona Spain

Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data-hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). the goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. the learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks.

关键词： Training Measurement Correlation Prediction algorithms Classification algorithms pattern recognition Labeling

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Toward a Better Text Data Augmentation via Filtering and Transforming Augmented Instances 6th

Toward a Better Text Data Augmentation via Filtering and Tra...

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6th China conference on Knowledge Graph and Semantic Computing (CCKS)

作者： Xia, Fei He, Shizhu Liu, Kang Liu, Shengping Zhao, Jun Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Beijing Unisound Informat Technol Beijing 100028 Peoples R China

ISBN: (纸本)9789811664717;9789811664700

thanks to a large amount of high-quality labeled data (instances), deep learning offers significant performance benefits in a variety of tasks. However, instance construction is very time-consuming and laborious, and it is a big challenge for natural language processing (NLP) tasks in many fields. For example, the instances of the question matching dataset CHIP in the medical field are only 2.7% of the general field dataset LCQMC, and its performance is only 79.19% of the general field. Due to the scarcity of instances, people often use methods such as data augmentation, robust learning, and the pre-trained model to alleviate this problem. Text data augmentation and pre-trained models are two of the most commonly used methods to solve this problem in NLP. However, current experiments have shown that the use of general data augmentation techniques may have limited or even negative effects on the pre-trained model. In order to fully understand the reasons for this result, this paper uses three types of data quality assessment methods from two levels of label-independent and label-dependent, and then select, filter and transform the results of the three text data augmentation methods. Our experiments on both generic and specialized (medical) fields have shown that through analysis, selection/filtering, and transformation of augmented instances, the performance of intent understanding and question matching in the pre-trained model can be effectively improved.

关键词： Text augmentation Pre-trained model Quality assessment

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Hyper vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit 7th

Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate...

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7th National conference on computer vision, pattern recognition, Image Processing, and Graphics, NCVPRIPG 2019

作者： Sabarinathan, D. Parisa Beham, M. Mansoor Roomi, S. M. Md. Couger Inc Tokyo Japan Sethu Institute of Technology VirudhunagarTamilnadu India Thiagarajar College of Engineering MaduraiTamil Nadu India

ISBN: (纸本)9789811586965

Challenges in accurate tumor detection paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Edge-Aware Monocular Dense Depth Estimation with Morphology

Edge-Aware Monocular Dense Depth Estimation with Morphology

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International conference on pattern recognition

作者： Zhi Li Xiaoyang Zhu Haitao Yu Qi Zhang Yongshi Jiang Institute of Automation Chinese Academy of Sciences

Dense depth maps play an important role in computer vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. the depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

关键词： three-dimensional displays Simultaneous localization and mapping Image edge detection Morphology Estimation User experience pattern recognition

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