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arXiv 2021年

作者： Yang, Ze Wang, Haofei Lu, Feng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Peng Cheng Laboratory Shenzhen518055 China

Remote photoplethysmography (rPPG) monitors heart rate without requiring physical contact, which allows for a wide variety of applications. Deep learning-based rPPG have demonstrated superior performance over the traditional approaches in controlled context. However, the lighting situation in indoor space is typically complex, with uneven light distribution and frequent variations in illumination. It lacks a fair comparison of different methods under different illuminations using the same dataset. In this paper, we present a public dataset, namely the BH-rPPG dataset, which contains data from thirty five subjects under three illuminations: low, medium, and high illumination. We also provide the ground truth heart rate measured by an oximeter. We evaluate the performance of three deep learning-based methods (Deepphys, rPPGNet, and Physnet) to that of four traditional methods (CHROM, GREEN, ICA, and POS) using two public datasets: the UBFC-rPPG dataset and the BH-rPPG dataset. The experimental results demonstrate that traditional methods are generally more resistant to fluctuating illuminations. We found that the Physnet achieves lowest mean absolute error (MAE) among deep learning-based method under medium illumination, whereas the CHROM achieves 1.04 beats per minute (BPM), outperforming the Physnet by 80%. Additionally, we investigate potential methods for improving performance of deep learning-based methods. We find that brightness augmentation make model more robust to variation illumination. These findings suggest that while developing deep learning-based heart rate estimation algorithms, illumination variation should be taken into account. This work serves as a benchmark for rPPG performance evaluation and it opens a pathway for future investigation into deep learning-based rPPG under illumination variations. Copyright © 2021, The Authors. All rights reserved.

关键词： Heart

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Mitigating Artifacts in Real-World Video Super-Resolution Models

arXiv

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arXiv 2022年

作者： Xie, Liangbin Wang, Xintao Shi, Shuwei Gu, Jinjin Dong, Chao Shan, Ying The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Macau China ARC Lab Tencent PCG China Shenzhen International Graduate School Tsinghua University China The University of Sydney Australia Shanghai Artificial Intelligence Laboratory China

The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and complex degradations, hidden states tend to contain unpleasant artifacts and propagate them to restored frames. In this circumstance, our analyses show that such artifacts can be largely alleviated when the hidden state is replaced with a cleaner counterpart. Based on the observations, we propose a Hidden State Attention (HSA) module to mitigate artifacts in real-world video super-resolution. Specifically, we first adopt various cheap filters to produce a hidden state pool. For example, Gaussian blur filters are for smoothing artifacts while sharpening filters are for enhancing details. To aggregate a new hidden state that contains fewer artifacts from the hidden state pool, we devise a Selective Cross Attention (SCA) module, in which the attention between input features and each hidden state is calculated. Equipped with HSA, our proposed method, namely FastRealVSR, is able to achieve 2× speedup while obtaining better performance than Real-BasicVSR. Codes will be available at https://***/TencentARC/FastRealVSR. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Transferable targeted adversarial attack via multi-source perturbation generation and integration

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Journal of Visual Communication and Image Representation 2025年 111卷

作者： Zhang, Shihui Han, Shaojie Yang, Sheng Han, Xueqiang Su, Junbin Zhai, Gangzheng Wang, Houlin School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China Hebei Key Laboratory of Computer Virtual Technology and System Integration Hebei Qinhuangdao China

With the rapid development of artificial intelligence, deep learning models have been applied in the field of society (e.g., video or image representation). However, due to the presence of adversarial examples, these models exhibit obvious fragility, which has become a major challenge restricting society development. Therefore, studying the generation process and achieving high transferability of adversarial examples are of utmost importance. In this paper, we propose a transferable targeted adversarial attack method called Multi-source Perturbation Generation and Integration (MPGI) to address the vulnerability and uncertainty of deep learning models. Specifically, MPGI consists of three critical designs to achieve targeted transferability of adversarial examples. Firstly, we propose a Collaborative Feature Fusion (CFF) component, which reduces the impact of original example feature on model classification by considering collaboration in feature fusion. Subsequently, we propose a Multi-scale Perturbation Dynamic Fusion (MPDF) module to fuse perturbations from different scales for enriching perturbation diversity. Finally, we innovatively investigate a novel Logit Margin with Penalty (LMP) loss to further enhance the misleading ability of the examples. The LMP, as a pluggable part, offers the potential to be leveraged by different approaches for boosting performance. In summary, MPGI can effectively achieve targeted attacks, expose the shortcomings of existing models, and promote the development of artificial intelligence in terms of security. Extensive experiments on ImageNet-Compatible and CIFAR-10 datasets demonstrate the superiority of the proposed method. For instance, the attack success rate increases by 17.6% and 17.0% compared to state-of-the-art method when transferred from DN-121 to Inc-v3 and MB-v2 models. © 2025 Elsevier Inc.

关键词： Adversarial attacks Feature fusion Logit margin Perturbation generation Targeted transferability

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A New Behavior-Assisted Semantic Recognition Method for Smart Home

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China Communications 2020年第6期17卷 26-36页

作者： Weijin Jiang Xiaoliang Liu Xingbao Liu Yang Wang Sijian Lv Fang Ye School of Computer Science and Technology Hunan University of Technology and BusinessChangsha 410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha 410205China Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan ProvinceHunan University of Technology and BusinessChangsha 410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430073China

In the recent Smart Home(SH)research work,intelligent service recommendation technique based on behavior recognition,it has been extensively preferred by ***,most current research uses the Semantic recognition to construct the user’s basic behavior *** method is usually restricted by environmental factors,the way these models are built makes it impossible for them to dynamically match the services that might be provided in the user *** solve this problem,this paper proposes a Semantic behavior assistance(Semantic behavior assistance,SBA).By joining the semantic model on the intelligent gateway,building an SA model,in this way,a logical Internet networks for smart home is *** the same time,a behavior assistant method based on SBA model is proposed,among them,the user environment-related entities,sensors,devices,and user-related knowledge models exist in the logical interconnection network of the SH system through the semantic *** this paper,the data simulation experiment is carried out on the *** experimental results show that the SBA model is better than the knowledge-based pre-defined model.

关键词： smart home(SH) semantic behavior assistance(SBA) user behavior intention behavior assisted method

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Mobile Internet Mobile Agent System Dynamic Trust Model for Cloud Computing

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computers, Materials & Continua 2020年第1期62卷 123-136页

作者： Weijin Jiang Yang Wang Yirong Jiang Yuhui Xu Jiahui Chen Lina Tan Guo Liang Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Hunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China Tonghua Normal University Tonghua134002China School of Bioinformatics University of MinnesotaTwin CitiesUSA

In mobile cloud computing,trust is a very important parameter in mobile cloud computing security because data storage and data processing are performed remotely in the *** at the security and trust management of mobile agent system in mobile cloud computing environment,the Human Trust Mechanism(HTM)is used to study the subjective trust formation,trust propagation and trust evolution law,and the subjective trust dynamic management algorithm(MASTM)is *** on the interaction experience between the mobile agent and the execution host and the third-party recommendation information to collect the basic trust data,the public trust host selection algorithm is *** isolated malicious host algorithm and the integrated trust degree calculation algorithm realize the function of selecting the trusted cluster and isolating the malicious host,so as to enhance the security interaction between the mobile agent and the *** algorithm simulation and verification were carried out to prove its feasibility and effectiveness.

关键词： Cloud computing mobile agent system subjective trust objective trust dynamic trust management mobile Internet

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Acknowledging the Unknown for Multi-label Learning with Single Positive Labels

arXiv

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arXiv 2022年

作者： Zhou, Donghao Chen, Pengfei Wang, Qiong Chen, Guangyong Heng, Pheng-Ann Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Tencent Technology Shenzhen China Zhejiang Lab Hangzhou China The Chinese University of Hong Kong Hong Kong

Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels. We consider an extreme of this weakly supervised learning problem, called single positive multi-label learning (SPML), where each multi-label training image has only one positive label. Traditionally, all unannotated labels are assumed as negative labels in SPML, which introduces false negative labels and causes model training to be dominated by assumed negative labels. In this work, we choose to treat all unannotated labels from an alternative perspective, i.e. acknowledging they are unknown. Hence, we propose entropy-maximization (EM) loss to attain a special gradient regime for providing proper supervision signals. Moreover, we propose asymmetric pseudo-labeling (APL), which adopts asymmetric-tolerance strategies and a self-paced procedure, to cooperate with EM loss and then provide more precise supervision. Experiments show that our method significantly improves performance and achieves state-of-the-art results on all four benchmarks. Code is available at https://***/CorrerZhou/SPML-AckTheUnknown. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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

arXiv

引用

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

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Research on Recommendation Strategies Integrating Emotional Tendency and User Influences 20

Research on Recommendation Strategies Integrating Emotional ...

引用

2020 International Conference on Internet Computing for Science and Engineering, ICICSE 2020 and the 13th International Conference on Advanced computer Theory and Engineering, ICACTE 2020

作者： Jing, Chen Liuqian, Duan Hong, Zhang College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Software Engineering Laboratory in Hebei Province Qinhuangdao China

ISBN: (纸本)9781450377348

At present, the topic model based on LDA as an information recommendation method has the defect of neglecting the emotional information in social platform. Therefore, a microblog user recommendation strategy I-TES (influence topic emotionality similarity model) is proposed which combines emotional orientation with user influence model in this paper. Firstly, the user's theme distribution and emotional tendency is obtained based on JST model, the JS distance is used to calculate the similarity of user's theme probability distribution, and the two is combined to get the user's interest similarity score. Secondly, the user's influence measurement indicators are summarized into microblog influence, user's activity and fan's influence, and the weights of the three indicators are calculated according to AHP(analytic hierarchy process). Finally, the combination of the two is used to obtain the final score as the recommendation result. The strategy is tested on Sina Weibo user data set, and the experiment shows that this strategy has higher recommendation accuracy than the traditional recommendation method. © 2020 ACM.

关键词： Probability distributions

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Heterogeneous relational complement for vehicle re-identification

arXiv

引用

arXiv 2021年

作者： Zhao, Jiajian Zhao, Yifan Li, Jia Yan, Ke Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems Scse Beihang University Department of Computer Science and Technology Peking University Tencent Youtu Lab Shanghai China Peng Cheng Laboratory Shenzhen China

The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve this problem from two aspects: constructing robust feature representations and proposing camerasensitive evaluations. We first propose a novel Heterogeneous Relational Complement Network (HRCN) by incorporating region-specific features and cross-level features as complements for the original high-level output. Considering the distributional differences and semantic misalignment, we propose graph-based relation modules to embed these heterogeneous features into one unified highdimensional space. On the other hand, considering the deficiencies of cross-camera evaluations in existing measures (i.e., CMC and AP), we then propose a Cross-camera Generalization Measure (CGM) to improve the evaluations by introducing position-sensitivity and cross-camera generalization penalties. We further construct a new benchmark of existing models with our proposed CGM and experimental results reveal that our proposed HRCN model achieves new state-of-the-art in VeRi-776, VehicleID, and VERI-Wild. Copyright © 2021, The Authors. All rights reserved.

关键词： Cameras

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Correction: Enumerating all multi-constrained s–t paths on temporal graph

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Knowledge and Information Systems 2024年第5期66卷 3157-3157页

作者： Jin, Yue Chen, Zijun Liu, Wenyuan School of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

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