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2016 IEEE Conference on computer vision and Pattern Recognition, CVPR 2016

作者： Zhang, Bowen Wang, Limin Wang, Zhe Qiao, Yu Wang, Hanli Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Computer Vision Lab ETH Zurich Switzerland

ISBN: (纸本)9781467388511

The deep two-stream architecture [23] exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation. However, motion vector lacks fine structures, and contains noisy and inaccurate motion patterns, leading to the evident degradation of recognition performance. Our key insight for relieving this problem is that optical flow and motion vector are inherent correlated. Transferring the knowledge learned with optical flow CNN to motion vector CNN can significantly boost the performance of the latter. Specifically, we introduce three strategies for this, initialization transfer, supervision transfer and their combination. Experimental results show that our method achieves comparable recognition performance to the state-of-the-art, while our method can process 390.7 frames per second, which is 27 times faster than the original two-stream method.

关键词： Pattern recognition

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Gated SwitchGAN for multi-domain facial image translation

arXiv

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arXiv 2021年

作者： Zhang, Xiaokang Zhu, Yuanlue Chen, Wenting Liu, Wenshuang Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Recent studies on multi-domain facial image translation have achieved impressive results. The existing methods generally provide a discriminator with an auxiliary classifier to impose domain translation. However, these methods neglect important information regarding domain distribution matching. To solve this problem, we propose a switch generative adversarial network (SwitchGAN) with a more adaptive discriminator structure and a matched generator to perform delicate image translation among multiple domains. A feature-switching operation is proposed to achieve feature selection and fusion in our conditional modules. We demonstrate the effectiveness of our model. Furthermore, we also introduce a new capability of our generator that represents attribute intensity control and extracts content information without tailored training. Experiments on the Morph, RaFD and CelebA databases visually and quantitatively show that our extended SwitchGAN (i.e., Gated SwitchGAN) can achieve better translation results than StarGAN, AttGAN and STGAN. The attribute classification accuracy achieved using the trained ResNet-18 model and the FID score obtained using the ImageNet pretrained Inception-v3 model also quantitatively demonstrate the superior performance of our models. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Scene Consistency Representation Learning for Video Scene Segmentation

arXiv

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arXiv 2022年

作者： Wu, Haoqian Chen, keyu Luo, Yanan Qiao, Ruizhi Ren, Bo Liu, Haozhe Xie, Weicheng Shen, Linlin Computer Vision Institute Shenzhen University China Tencent YouTu Lab Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing China KAUST Saudi Arabia

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available. © 2022, CC BY.

关键词： Semantics

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PNet: An Efficient Network for Pneumonia Detection 12

PNet: An Efficient Network for Pneumonia Detection

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Li, Zhongliang Yu, Juan Li, Xuechen Li, Yingqi Dai, Weicai Shen, Linlin Mou, Lisha Pu, Zuhui Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Institute of Translational Medicine Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Imaging Department Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Shenzhen Second People's Hospital First Affiliated Hospital Shenzhen University Shenzhen China Imaging Department Shenzhen Bao'an District Songgang People's Hospital Shenzhen China Imaging Department Fifth People's Hospital of Longgang District Shenzhen China

ISBN: (纸本)9781728148526

Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters). © 2019 IEEE.

关键词： Deep neural networks

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Position based catheterization and angiography simulation 6

Position based catheterization and angiography simulation

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6th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2018

作者： Zuo, Zhihao Si, Weixin Qian, Weixin Yinling Liao, Xiangyun Heng, Pheng-Ann Department of Information Engineering Wuhan University of Technology Wuhan China Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781538662984

Effective and safe performance of cardiovascular interventions requires excellent catheter / guidewire manipulation skills. These skills are currently mainly gained through an apprenticeship on real patients, which may not be safe or cost-effective. computer simulation offers an alternative for core skills training. However, replicating the physical behaviour of real instruments navigated through blood vessels is a challenging task. In this work, we adopt the position based method to fast and robust model the dynamic behavior of catheter / guidewire and contrast medium propagation in vascular interventional radiology. Specifically, we simulate the elastic rods according to the cosserat theory and the contrast medium propagation is controlled by an advection-diffusion equation. Besides, we employ bounding volume hierarchy (BVH) to achieve fast and accurate collision detection between vessels and rods and compute the response with the Coulomb's law. Experimental results demonstrate the visual plausibility, robustness and efficiency of our method in interactive catheterization simulation. © 2018 IEEE.

关键词： Catheters

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GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure Detection

GV-Bench: Benchmarking Local Feature Matching for Geometric ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jingwen Yu Hanjing Ye Jianhao Jiao Ping Tan Hong Zhang The Hong Kong University of Science and Technology Hong Kong SAR China Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Computer Science University College London London The United Kingdom

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Visual loop closure detection is an important module in visual simultaneous localization and mapping (SLAM), which associates current camera observation with previously visited places. Loop closures correct drifts in trajectory estimation to build a globally consistent map. However, a false loop closure can be fatal, so verification is required as an additional step to ensure robustness by rejecting the false positive loops. Geometric verification has been a well-acknowledged solution that leverages spatial clues provided by local feature matching to find true positives. Existing feature matching methods focus on homography and pose estimation in long-term visual localization, lacking references for geometric verification. To fill the gap, this paper proposes a unified benchmark targeting geometric verification of loop closure detection under long-term conditional variations. Furthermore, we evaluate six representative local feature matching methods (handcrafted and learning-based) under the benchmark, with in-depth analysis for limitations and future directions.

关键词： Location awareness Visualization Simultaneous localization and mapping Pose estimation Benchmark testing Feature extraction Cameras Robustness Trajectory Intelligent robots

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Poster: Towards Accurate and Fast Federated Learning in End-Edge-Cloud Orchestrated Networks 43

Poster: Towards Accurate and Fast Federated Learning in End-...

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43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023

作者： Li, Mingze Sun, Peng Zhou, Huan Zhao, Liang Liu, Xuxun Leung, Victor C. M. College of Computer and Information Technology China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350339864

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks. © 2023 IEEE.

关键词： Synchronization

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Ontology-Based BERT Model for Automated Information Extraction from Geological Hazard Reports

引用

Journal of Earth Science 2023年第5期34卷 1390-1405页

作者： Kai Ma Miao Tian Yongjian Tan Qinjun Qiu Zhong Xie Rong Huang Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Engineering Technology Research Center for Farmland Environment Monitoring China Three Gorges UniversityYichang 443002China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China College of Economics and Management China Three Gorges UniversityYichang 443002China

Geological knowledge can provide support for knowledge discovery, knowledge inference and mineralization predictions of geological big data. Entity identification and relationship extraction from geological data description text are the key links for constructing knowledge graphs. Given the lack of publicly annotated datasets in the geology domain, this paper illustrates the construction process of geological entity datasets, defines the types of entities and interconceptual relationships by using the geological entity concept system, and completes the construction of the geological corpus. To address the shortcomings of existing language models(such as Word2vec and Glove) that cannot solve polysemous words and have a poor ability to fuse contexts, we propose a geological named entity recognition and relationship extraction model jointly with Bidirectional Encoder Representation from Transformers(BERT) pretrained language model. To effectively represent the text features, we construct a BERT-bidirectional gated recurrent unit network(BiGRU)-conditional random field(CRF)-based architecture to extract the named entities and the BERT-BiGRU-Attention-based architecture to extract the entity relations. The results show that the F1-score of the BERT-BiGRU-CRF named entity recognition model is 0.91 and the F1-score of the BERT-BiGRU-Attention relationship extraction model is 0.84, which are significant performance improvements when compared to classic language models(e.g., word2vec and Embedding from Language Models(ELMo)).

关键词： ontology BERT model name entity recognition relation extraction knowledge graph

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SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Recognition

SATGAN: Augmenting Age Biased Dataset for Cross-Age Face Rec...

引用

International Conference on Pattern Recognition

作者： Wenshuang Liu Wenting Chen Yuanlue Zhu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

In this paper, we propose a Stable Age Translation GAN (SATGAN) to generate fake face images at different ages to augment age biased face datasets for Cross-Age Face Recognition (CAFR). The proposed SATGAN consists of both generator and discriminator. As a part of the generator, a novel Mask Attention Module (MAM) is introduced to make the generator focus on the face area. In addition, the generator employs a Uniform Distribution Discriminator (UDD) to supervise the learning of latent feature map and enforce the uniform distribution. Besides, the discriminator employs a Feature Separation Module (FSM) to disentangle identity information from the age information. The quantitative and qualitative evaluations on Morph dataset prove that SATGAN achieves much better performance than existing methods. The face recognition model trained using dataset (VGGFace2 and MS-Celeb-lM) augmented using our SATGAN achieves better accuracy on cross age dataset like Cross-Age LFW and AgeDB-30.

关键词： Training Face recognition Generators

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