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International Conference on Automatic Face and Gesture Recognition

作者： Ziyi Bai Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665431774

Set-based Face Recognition is widely applied in scenarios like law enforcement and online media data management. Compared with face recognition using a single image, the faces in the set often contain abundant appearance changes. Therefore, how to make full use of the rich information from the set and integrate them into a unified set representation become the key to set-based face recognition. Inspired by the fact that humans usually complete this fine-grained task through integrating the information from the congruent local regions (e.g. an eye to an eye) of multiple faces in a set, we propose a novel method called Local Feature Enhancement Network (LFENet), which can automatically enhance the local feature through transferring the local information across the images. Specifically, we retain the spatial semantic information of the feature maps and apply different relational functions to establish the correlation among the local features. The contained local information will be transferred to the relevant local features to enhance their discriminability. By doing so, the valuable local information carried in some local features can complement those with incomplete information. Besides, the various local information is aligned across faces under different conditions to help the model learn intra-set-compact face representations. Our method achieves state-of-the-art performances on two mainstream set-based face recognition benchmarks: IJB-A and IJB-C, which fully reflects the rationality and effectiveness of our local feature enhancement mechanism.

关键词： Correlation Law enforcement Face recognition Conferences Semantics Gesture recognition Media

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WeChat AI & ICT's submission for DSTC9 interactive dialogue evaluation track

arXiv

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arXiv 2021年

作者： Li, Zekang Li, Zongjia Zhang, Jinchao Feng, Yang Zhou, Jie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China WeChat AI Tencent Inc China School of EECS Peking University China University of Chinese Academy of Sciences China

We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue). In sub-task 1, we employ a pre-trained language model to generate topic-related responses and propose a response ensemble method for response selection. In sub-task2, we propose a novel Dialogue Planning Model (DPM) to capture conversation flow in the interaction with humans. We also design an integrated open-domain dialogue system containing pre-process, dialogue model, scoring model, and post-process, which can generate fluent, coherent, consistent, and human-like responses. We tie 1st on human ratings and also get the highest Meteor, and Bert-score in sub-task 1, and rank 3rd on interactive human evaluation in sub-task 2. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech processing

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Topic Scene Graph Generation by Attention Distillation from Caption

Topic Scene Graph Generation by Attention Distillation from ...

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International Conference on Computer Vision (ICCV)

作者： Wenbin Wang Ruiping Wang Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665428132

If an image tells a story, the image caption is the briefest narrator. Generally, a scene graph prefers to be an omniscient "generalist", while the image caption is more willing to be a "specialist", which outlines the gist. Lots of previous studies have found that a scene graph is not as practical as expected unless it can reduce the trivial contents and noises. In this respect, the image caption is a good tutor. To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the socalled Topic Scene Graph. What an image caption pays attention to is distilled and passed to the scene graph for estimating the importance of partial objects, relationships, and events. Specifically, during the caption generation, the attention about individual objects in each time step is collected, pooled, and assembled to obtain the attention about relationships, which serves as weak supervision for regularizing the estimated importance scores of relationships. In addition, as this attention distillation process provides an opportunity for combining the generation of image caption and scene graph together, we further transform the scene graph into linguistic form with rich and free-form expressions by sharing a single generation model with image caption. Experiments show that attention distillation brings significant improvements in mining important relationships without strong supervision, and the topic scene graph shows great potential in subsequent applications.

关键词： Computer vision Transforms Linguistics Guidelines

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BVPNet: Video-to-BVP Signal Prediction for Remote Heart Rate Estimation

BVPNet: Video-to-BVP Signal Prediction for Remote Heart Rate...

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International Conference on Automatic Face and Gesture Recognition

作者： Abhijit Das Hao Lu Hu Han Antitza Dantcheva Shiguang Shan Xilin Chen Thapar Institute of Engineering & Technology Thapar University Patiala India Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS China Inria Sophia Antipolis France Université Côte d'Azur France

ISBN: (纸本)9781665431774

In this paper, we propose a new method for remote photoplethysmography (rPPG) based heart rate (HR) estimation. In particular, our proposed method BVPNet is streamlined to predict the blood volume pulse (BVP) signals from face videos. Towards this, we firstly define ROIs based on facial landmarks and then extract the raw temporal signal from each ROI. Then the extracted signals are pre-processed via first-order difference and Butterworth filter and combined to form a Spatial-Temporal map (STMap). We then propose to revise U-Net, in order to predict BVP signals from the STMap. BVPNet takes into account both temporal and frequency domain losses in order to learn better than conventional models. Our experimental results suggest that our BVPNet outperforms the state-of-the-art methods on two publicly available datasets (MMSE-HR and VIPL-HR).

关键词： Heart rate Face recognition Frequency-domain analysis Conferences Estimation Gesture recognition Photoplethysmography

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Locality-constrained framework for face alignment

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Frontiers of Computer Science 2019年第4期13卷 789-801页

作者： Jie ZHANG Xiaowei ZHAO Meina KAN Shiguang SHAN Xiujuan CHAI Xilin CHEN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Alibaba Group Hangzhou 311121 China

Although the conventional active appearance model (AAM) has achieved some success for face alignment, it still suffers from the generalization problem when be applied to unseen subjects and images. To deal with the gem eralization problem of AAM, we first reformulate the original AAM as sparsity-regularized AAM, which can achieve more compact/better shape and appearance priors by selecting nearest neighbors as the bases of the shape and appearance model. To speed up the fitting procedure, the sparsity in sparsity?regularized AAM is approximated by using the locality (i.e., AT-nearest neighbor), and thus inducing the locality-constrained active appearance model (LC-AAM). The LC-AAM solves a constrained AAM-like fitting problem with the K-nearest neighbors as the bases of shape and appearance model. To alleviate the adverse influence of inaccurate AT-nearest neighbor results, the locality constraint is further embedded in the discriminative fitting method denoted as LC-DFM, which can find better K-nearest neighbor results by employing shape-indexed feature, and can also tolerate some inaccurate neighbors benefited from the regression model rather than the generative model in AAM. Extensive experiments on several datasets demonstrate that our methods outperform the state-of-the-arts in both detection accuracy and generalization ability.

关键词： locality-constrained AAM locality-constrained DFM face alignment sparsity-regularization

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Domain generalization for document authentication against practical recapturing attacks

arXiv

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arXiv 2021年

作者： Chen, Changsheng Zhang, Shuzheng Lan, Fengbo Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision *** Codes 94A08 Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Deep learning-based forgery attack on document images

arXiv

引用

arXiv 2021年

作者： Zhao, Lin Chen, Changsheng Huang, Jiwu the Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image in an end-to-end fashion. In this work, we present a low-cost document forgery algorithm by the existing deep learning-based technologies to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach in both complicated characters and complex texture. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authenticat

关键词： Textures

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Discriminative semantic feature pyramid network with guided anchoring for logo detection

arXiv

引用

arXiv 2021年

作者： Zhang, Baisong Min, Weiqing Wang, Jing Hou, Sujuan Hou, Qiang Zheng, Yuanjie Jiang, Shuqiang School of Information Science and Engineering Shandong Normal University Shandong250358 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Recently, logo detection has received more and more attention for its wide applications in the multimedia field, such as intellectual property protection, product brand management, and logo duration monitoring. Unlike general object detection, logo detection is a challenging task, especially for small logo objects and large aspect ratio logo objects in the real-world scenario. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). Considering that low-level feature maps that are used to detect small logo objects lack semantic information, we propose the DSFP, which can enrich more discriminative semantic features of low-level feature maps and can achieve better performance on small logo objects. Furthermore, preset anchor boxes are less efficient for detecting large aspect ratio logo objects. We therefore integrate the GA into our method to generate large aspect ratio anchor boxes to mitigate this issue. Extensive experimental results on four benchmarks demonstrate the effectiveness of our proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the advantage of our method in detecting small and large aspect logo objects. © 2021, CC BY.

关键词： Object detection

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Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

arXiv

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arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

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Real-Time Data Collection of Wsn Based on Trajectory Optimization of Laser-Charged Uav

SSRN

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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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