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An end-to-end foreground-aware network for person re-identification

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arXiv 2019年

作者： Liu, Yiheng Zhou, Wengang Liu, Jianzhuang Qi, Guojun Tian, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Noah’s Ark Lab Huawei Technologies Company Limited Shenzhen518129 China Huawei Cloud EI Product Department Cloud & AI Huawei Technologies

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

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arXiv 2022年

作者： Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Hong Kong Huawei Cloud Computing Zhejiang Lab China

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT;Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory- Augmented Pre-trained Language Model (GMAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP1 can achieve SOTA results on all tasks. © 2022, CC BY.

关键词： Computational linguistics

On-edge multi-task transfer learning: Model and practice with data-driven task allocation

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

Machine learning-based orchestration of containers: A taxonomy and future directions

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arXiv 2021年

作者： Zhong, Zhiheng Xu, Minxian Rodriguez, Maria Alejandra Xu, Chengzhong Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Grattan Street Parkville MelbourneVICVIC 3010 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University Town 1068 Xueyuan Avenue Guangdong Shenzhen518055 China State Key Lab of IOTSC University of Macau Avenida da Universidade Taipa 999078 China

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted. © 2021, CC0.

关键词： Containers

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

Stable Attribute Group Editing for Reliable Few-shot Image Generation

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arXiv 2023年

作者： Ding, Guanqi Han, Xinzhe Wang, Shuhui Wu, Shuzhe Jin, Xin Tu, Dandan Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China Huawei Cloud EI Innovation Lab China

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an "editing-based" framework Attribute Group Editing (AGE) for reliable few-shot image generation, which largely improves the performance compared with existing methods that require re-training a GAN with limited data. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. This paper investigates the class inconsistency problem and proposes Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. Different from AGE which directly edits from a one-shot image, SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary. Meanwhile, according to the projection weights on the category-relevant attribute dictionary, we can select category-irrelevant attributes from the similar seen categories. Consequently, SAGE injects the whole distribution of the novel class into StyleGAN's latent space, thus largely remains the category retention and stability of the generated images. Going one step further, we find that class inconsistency is a common problem in GAN-generated images for downstream classification. Even though the generated images look photo-realistic and requires no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generative model and classification model perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components. Extensive experime

关键词： Generative adversarial networks

Progressive Residual Extraction based Pre-training for Speech Representation Learning

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arXiv 2024年

作者： Wang, Tianrui Li, Jin Ma, Ziyang Cao, Rui Chen, Xie Wang, Longbiao Ge, Meng Wang, Xiaobao Wang, Yuguang Dang, Jianwu Tashi, Nyima The Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300350 China Company Ltd. Tianjin300350 China Saw Swee Hock School of Public Health National University of Singapore Singapore MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Co. Ltd. Tianjin China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China The School of Information Science and Technology Tibet University Lhasa China

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named PROGRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM. © 2024, CC BY.

关键词： Self-supervised learning

Dual-Protected Metal Halide Perovskite Nanosheets with an Enhanced Set of Stabilities

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Angewandte Chemie 2021年第13期133卷

作者： Dr. Yanjie He Yachao Liang Shuang Liang Dr. Yeu-Wei Harn Dr. Zili Li Mingyue Zhang Dingfeng Shen Zhiwei Li Prof. Yan Yan Prof. Xinchang Pang Prof. Zhiqun Lin School of Materials Science and Engineering Georgia Institute of Technology Atlanta GA 30332 USA Henan Joint International Research Laboratory of Living Polymerizations and Functional Nanomaterials Henan Key Laboratory of Advanced Nylon Materials and Application School of Materials Science and Engineering Zhengzhou University Zhengzhou 450001 China Shenzhen Cloud Computing Center National Supercomputing Center Shenzhen 518055 China

Approaches to achieve stable perovskite nanocrystals (PNCs) of interest, in particular those with large structural anisotropy, through protective coating of the inorganic shell at a single-nanocrystal (NC) level are comparatively few and limited in scope. Reported here is a robust amphiphilic-diblock-copolymer-enabled strategy for crafting highly-stable anisotropic CsPbBr 3 nanosheets (NSs) by in situ formation of a uniform inorganic shell (1st shielding) that is intimately ligated with hydrophobic polymers (2nd shielding). The dual-protected NSs display an array of remarkable stabilities (i.e., thermal, photostability, moisture, polar solvent, aliphatic amine, etc.) and find application in white-light-emitting diodes. In principle, by anchoring other multidentate amphiphilic polymer ligands on the surface of PNCs, followed by templated-growth of shell materials of interest, a rich variety of dual-shelled, multifunctional PNCs with markedly improved stabilities can be created for use in optics, optoelectronics, and sensory devices.

关键词： block copolymers perovskites materials science nanostructures semiconductors

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面向微博的情感影响最大化模型

面向微博的情感影响最大化模型

第29届中国数据库学术会议

作者： OU Gaoyan 欧高炎 CHEN Wei 陈薇 WANG Tengjiao 王腾蛟 LEI Kai 雷凯 YANG Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 Chin 高可信软件技术教育部重点实验室北京100871 北京大学信息科学技术学院北京100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School Peki 北京大学深圳研究生院深圳市云计算关键技术与应用重点实验室广东深圳518055

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型—... 详细信息

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型——情感影响分配模型(SID),证明了SID模型下的情感影响最大化问题是一个NP难问题,给出了一个具有精度保证的贪心算法。在真实的微博数据上的实验结果表明,SID模型能够有效地找出情感影响最大化的节点集,同时具有很高的扩展性。

关键词：社交网络情感分析信息传播模型分配算法