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doi: 10.1007/s11633-022-1410-8
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With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT), generative pre-trained transformers (GPT), etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models:https://github.com/wangxiao5791509/MultiModal_BigModels_Survey .
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT), generative pre-trained transformers (GPT), etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models:
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doi: 10.1007/s11633-022-1398-0
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With the growing awareness of data privacy, federated learning (FL) has gained increasing attention in recent years as a major paradigm for training models with privacy protection in mind, which allows building models in a collaborative but private way without exchanging data. However, most FL clients are currently unimodal. With the rise of edge computing, various types of sensors and wearable devices generate a large amount of data from different modalities, which has inspired research efforts in multimodal federated learning (MMFL). In this survey, we explore the area of MMFL to address the fundamental challenges of FL on multimodal data. First, we analyse the key motivations for MMFL. Second, the currently proposed MMFL methods are technically classified according to the modality distributions and modality annotations in MMFL. Then, we discuss the datasets and application scenarios of MMFL. Finally, we highlight the limitations and challenges of MMFL and provide insights and methods for future research.
With the growing awareness of data privacy, federated learning (FL) has gained increasing attention in recent years as a major paradigm for training models with privacy protection in mind, which allows building models in a collaborative but private way without exchanging data. However, most FL clients are currently unimodal. With the rise of edge computing, various types of sensors and wearable devices generate a large amount of data from different modalities, which has inspired research efforts in multimodal federated learning (MMFL). In this survey, we explore the area of MMFL to address the fundamental challenges of FL on multimodal data. First, we analyse the key motivations for MMFL. Second, the currently proposed MMFL methods are technically classified according to the modality distributions and modality annotations in MMFL. Then, we discuss the datasets and application scenarios of MMFL. Finally, we highlight the limitations and challenges of MMFL and provide insights and methods for future research.
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doi: 10.1007/s11633-022-1396-2
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This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal
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doi: 10.1007/s11633-022-1393-5
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Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another. Recently, Transformer-based neural machine translation (NMT) has achieved great breakthroughs and has become a new mainstream method in both methodology and applications. In this article, we conduct an overview of Transformer-based NMT and its extension to other tasks. Specifically, we first introduce the framework of Transformer, discuss the main challenges in NMT and list the representative methods for each challenge. Then, the public resources and toolkits in NMT are listed. Meanwhile, the extensions of Transformer in other tasks, including the other natural language processing tasks, computer vision tasks, audio tasks and multi-modal tasks, are briefly presented. Finally, possible future research directions are suggested.
Machine translation is an important and challenging task that aims at automatically translating natural language sentences from one language into another. Recently, Transformer-based neural machine translation (NMT) has achieved great breakthroughs and has become a new mainstream method in both methodology and applications. In this article, we conduct an overview of Transformer-based NMT and its extension to other tasks. Specifically, we first introduce the framework of Transformer, discuss the main challenges in NMT and list the representative methods for each challenge. Then, the public resources and toolkits in NMT are listed. Meanwhile, the extensions of Transformer in other tasks, including the other natural language processing tasks, computer vision tasks, audio tasks and multi-modal tasks, are briefly presented. Finally, possible future research directions are suggested.
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doi: 10.1007/s11633-022-1395-3
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With the application of mobile communication technology in the automotive industry, intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted. The road and traffic information perceived by intelligent vehicles has important potential application value, especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic. Therefore, a type of vehicle control technology called predictive cruise control (PCC) has become a hot research topic. It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system. Most existing reviews focus on the economical driving of vehicles, but few scholars have conducted a comprehensive survey of PCC from theory to the status quo. In this paper, the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature, and typical applications under a cloud control system (CCS) are proposed. Firstly, the methodology of PCC is generally introduced. Then according to typical scenarios, the PCC-related research is deeply surveyed, including freeway and urban traffic scenarios involving traditional vehicles, new energy vehicles, intelligent vehicles, and multi-vehicle platoons. Finally, the general architecture and three typical applications of the cloud control system (CCS) on PCC are briefly introduced, and the prospect and future trends of PCC are proposed.
With the application of mobile communication technology in the automotive industry, intelligent connected vehicles equipped with communication and sensing devices have been rapidly promoted. The road and traffic information perceived by intelligent vehicles has important potential application value, especially for improving the energy-saving and safe-driving of vehicles as well as the efficient operation of traffic. Therefore, a type of vehicle control technology called predictive cruise control (PCC) has become a hot research topic. It fully taps the perceived or predicted environmental information to carry out predictive cruise control of vehicles and improves the comprehensive performance of the vehicle-road system. Most existing reviews focus on the economical driving of vehicles, but few scholars have conducted a comprehensive survey of PCC from theory to the status quo. In this paper, the methods and advances of PCC technologies are reviewed comprehensively by investigating the global literature, and typical applications under a cloud control system (CCS) are proposed. Firstly, the methodology of PCC is generally introduced. Then according to typical scenarios, the PCC-related research is deeply surveyed, including freeway and urban traffic scenarios involving traditional vehicles, new energy vehicles, intelligent vehicles, and multi-vehicle platoons. Finally, the general architecture and three typical applications of the cloud control system (CCS) on PCC are briefly introduced, and the prospect and future trends of PCC are proposed.
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doi: 10.1007/s11633-022-1364-x
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Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high-profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.
Lung cancer is the leading cause of cancer-related deaths worldwide. Medical imaging technologies such as computed tomography (CT) and positron emission tomography (PET) are routinely used for non-invasive lung cancer diagnosis. In clinical practice, physicians investigate the characteristics of tumors such as the size, shape and location from CT and PET images to make decisions. Recently, scientists have proposed various computational image features that can capture more information than that directly perceivable by human eyes, which promotes the rise of radiomics. Radiomics is a research field on the conversion of medical images into high-dimensional features with data-driven methods to help subsequent data mining for better clinical decision support. Radiomic analysis has four major steps: image preprocessing, tumor segmentation, feature extraction and clinical prediction. Machine learning, including the high-profile deep learning, facilitates the development and application of radiomic methods. Various radiomic methods have been proposed recently, such as the construction of radiomic signatures, tumor habitat analysis, cluster pattern characterization and end-to-end prediction of tumor properties. These methods have been applied in many studies aiming at lung cancer diagnosis, treatment and monitoring, shedding light on future non-invasive evaluations of the nodule malignancy, histological subtypes, genomic properties and treatment responses. In this review, we summarized and categorized the studies on the general workflow, methods for clinical prediction and clinical applications of machine learning in lung cancer radiomic studies, introduced some commonly-used software tools, and discussed the limitations of current methods and possible future directions.
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doi: 10.1007/s11633-022-1356-x
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Due to the complexity of emotional expression, recognizing emotions from the speech is a critical and challenging task. In most of the studies, some specific emotions are easily classified incorrectly. In this paper, we propose a new framework that integrates cascade attention mechanism and joint loss for speech emotion recognition (SER), aiming to solve feature confusions for emotions that are difficult to be classified correctly. First, we extract the mel frequency cepstrum coefficients (MFCCs), deltas, and delta-deltas from MFCCs to form 3-dimensional (3D) features, thus effectively reducing the interference of external factors. Second, we employ spatiotemporal attention to selectively discover target emotion regions from the input features, where self-attention with head fusion captures the long-range dependency of temporal features. Finally, the joint loss function is employed to distinguish emotional embeddings with high similarity to enhance the overall performance. Experiments on interactive emotional dyadic motion capture (IEMOCAP) database indicate that the method achieves a positive improvement of 2.49% and 1.13% in weighted accuracy (WA) and unweighted accuracy (UA), respectively, compared to the state-of-the-art strategies.
Due to the complexity of emotional expression, recognizing emotions from the speech is a critical and challenging task. In most of the studies, some specific emotions are easily classified incorrectly. In this paper, we propose a new framework that integrates cascade attention mechanism and joint loss for speech emotion recognition (SER), aiming to solve feature confusions for emotions that are difficult to be classified correctly. First, we extract the mel frequency cepstrum coefficients (MFCCs), deltas, and delta-deltas from MFCCs to form 3-dimensional (3D) features, thus effectively reducing the interference of external factors. Second, we employ spatiotemporal attention to selectively discover target emotion regions from the input features, where self-attention with head fusion captures the long-range dependency of temporal features. Finally, the joint loss function is employed to distinguish emotional embeddings with high similarity to enhance the overall performance. Experiments on interactive emotional dyadic motion capture (IEMOCAP) database indicate that the method achieves a positive improvement of 2.49% and 1.13% in weighted accuracy (WA) and unweighted accuracy (UA), respectively, compared to the state-of-the-art strategies.
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doi: 10.1007/s11633-023-1432-x
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Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.
Cancelable biometrics are a group of techniques to transform the input biometric to an irreversible feature intentionally using a transformation function and usually a key in order to provide security and privacy in biometric recognition systems. This transformation is repeatable enabling subsequent biometric comparisons. This paper introduces a new idea to be exploited as a transformation function for cancelable biometrics aimed at protecting templates against iterative optimization attacks. Our proposed scheme is based on time-varying keys (random biometrics in our case) and morphing transformations. An experimental implementation of the proposed scheme is given for face biometrics. The results confirm that the proposed approach is able to withstand leakage attacks while improving the recognition performance.
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doi: 10.1007/s11633-023-1430-z
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3D shape recognition has drawn much attention in recent years. The view-based approach performs best of all. However, the current multi-view methods are almost all fully supervised, and the pretraining models are almost all based on ImageNet. Although the pretraining results of ImageNet are quite impressive, there is still a significant discrepancy between multi-view datasets and ImageNet. Multi-view datasets naturally retain rich 3D information. In addition, large-scale datasets such as ImageNet require considerable cleaning and annotation work, so it is difficult to regenerate a second dataset. In contrast, unsupervised learning methods can learn general feature representations without any extra annotation. To this end, we propose a three-stage unsupervised joint pretraining model. Specifically, we decouple the final representations into three fine-grained representations. Data augmentation is utilized to obtain pixel-level representations within each view. And we boost the spatial invariant features from the view level. Finally, we exploit global information at the shape level through a novel extract-and-swap module. Experimental results demonstrate that the proposed method gains significantly in 3D object classification and retrieval tasks, and shows generalization to cross-dataset tasks.
3D shape recognition has drawn much attention in recent years. The view-based approach performs best of all. However, the current multi-view methods are almost all fully supervised, and the pretraining models are almost all based on ImageNet. Although the pretraining results of ImageNet are quite impressive, there is still a significant discrepancy between multi-view datasets and ImageNet. Multi-view datasets naturally retain rich 3D information. In addition, large-scale datasets such as ImageNet require considerable cleaning and annotation work, so it is difficult to regenerate a second dataset. In contrast, unsupervised learning methods can learn general feature representations without any extra annotation. To this end, we propose a three-stage unsupervised joint pretraining model. Specifically, we decouple the final representations into three fine-grained representations. Data augmentation is utilized to obtain pixel-level representations within each view. And we boost the spatial invariant features from the view level. Finally, we exploit global information at the shape level through a novel extract-and-swap module. Experimental results demonstrate that the proposed method gains significantly in 3D object classification and retrieval tasks, and shows generalization to cross-dataset tasks.
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doi: 10.1007/s11633-022-1386-4
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Cross-modal image-text retrieval is a fundamental task in bridging vision and language. It faces two main challenges that are typically not well addressed in previous works. 1) Generalizability: Existing methods often assume a strong semantic correlation between each text-image pair, which are thus difficult to generalize to real-world scenarios where the weak correlation dominates. 2) Efficiency: Many latest works adopt the single-tower architecture with heavy detectors, which are inefficient during the inference stage because the costly computation needs to be repeated for each text-image pair. In this work, to overcome these two challenges, we propose a two-tower cross-modal contrastive learning (CMCL) framework. Specifically, we first devise a two-tower architecture, which enables a unified feature space for the text and image modalities to be directly compared with each other, alleviating the heavy computation during inference. We further introduce a simple yet effective module named multi-grid split (MGS) to learn fine-grained image features without using detectors. Last but not the least, we deploy a cross-modal contrastive loss on the global image/text features to learn their weak correlation and thus achieve high generalizability. To validate that our CMCL can be readily generalized to real-world scenarios, we construct a large multi-source image-text dataset called weak semantic correlation dataset (WSCD). Extensive experiments show that our CMCL outperforms the state-of-the-arts while being much more efficient.
Cross-modal image-text retrieval is a fundamental task in bridging vision and language. It faces two main challenges that are typically not well addressed in previous works. 1) Generalizability: Existing methods often assume a strong semantic correlation between each text-image pair, which are thus difficult to generalize to real-world scenarios where the weak correlation dominates. 2) Efficiency: Many latest works adopt the single-tower architecture with heavy detectors, which are inefficient during the inference stage because the costly computation needs to be repeated for each text-image pair. In this work, to overcome these two challenges, we propose a two-tower cross-modal contrastive learning (CMCL) framework. Specifically, we first devise a two-tower architecture, which enables a unified feature space for the text and image modalities to be directly compared with each other, alleviating the heavy computation during inference. We further introduce a simple yet effective module named multi-grid split (MGS) to learn fine-grained image features without using detectors. Last but not the least, we deploy a cross-modal contrastive loss on the global image/text features to learn their weak correlation and thus achieve high generalizability. To validate that our CMCL can be readily generalized to real-world scenarios, we construct a large multi-source image-text dataset called weak semantic correlation dataset (WSCD). Extensive experiments show that our CMCL outperforms the state-of-the-arts while being much more efficient.
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doi: 10.1007/s11633-023-1423-y
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Great progress has been made toward accurate face detection in recent years. However, the heavy model and expensive computation costs make it difficult to deploy many detectors on mobile and embedded devices where model size and latency are highly constrained. In this paper, we present a millisecond-level anchor-free face detector, YuNet, which is specifically designed for edge devices. There are several key contributions in improving the efficiency-accuracy trade-off. First, we analyse the influential state-of-the-art face detectors in recent years and summarize the rules to reduce the size of models. Then, a lightweight face detector, YuNet, is introduced. Our detector contains a tiny and efficient feature extraction backbone and a simplified pyramid feature fusion neck. To the best of our knowledge, YuNet has the best trade-off between accuracy and speed. It has only 75 856 parameters and is less than 1/5 of other small-size detectors. In addition, a training strategy is presented for the tiny face detector, and it can effectively train models with the same distribution of the training set. The proposed YuNet achieves 81.1% mAP (single-scale) on the WIDER FACE validation hard track with a high inference efficiency (Intel i7-12700K: 1.6 ms per frame at 320×320). Because of its unique advantages, the repository for YuNet and its predecessors has been popular at GitHub and gained more than 11 K stars athttps://github.com/ShiqiYu/libfacedetection
Great progress has been made toward accurate face detection in recent years. However, the heavy model and expensive computation costs make it difficult to deploy many detectors on mobile and embedded devices where model size and latency are highly constrained. In this paper, we present a millisecond-level anchor-free face detector, YuNet, which is specifically designed for edge devices. There are several key contributions in improving the efficiency-accuracy trade-off. First, we analyse the influential state-of-the-art face detectors in recent years and summarize the rules to reduce the size of models. Then, a lightweight face detector, YuNet, is introduced. Our detector contains a tiny and efficient feature extraction backbone and a simplified pyramid feature fusion neck. To the best of our knowledge, YuNet has the best trade-off between accuracy and speed. It has only 75 856 parameters and is less than 1/5 of other small-size detectors. In addition, a training strategy is presented for the tiny face detector, and it can effectively train models with the same distribution of the training set. The proposed YuNet achieves 81.1% mAP (single-scale) on the WIDER FACE validation hard track with a high inference efficiency (Intel i7-12700K: 1.6 ms per frame at 320×320). Because of its unique advantages, the repository for YuNet and its predecessors has been popular at GitHub and gained more than 11 K stars at
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doi: 10.1007/s11633-022-1403-7
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Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques′ vulnerability to attack and poor performance in low-quality data. However, difficulties still exist in how to unify the meaning of heterogeneous scores more effectively. Aside from the matching scores themselves, the importance of the ranking information they include has been undervalued in previous studies. This study concentrates on matching scores and their ranking information and suggests the ranking partition collision (RPC) theory from the standpoint of the worth of scores. To meet both forensic and judicial needs, this paper proposes a method that employs a neural network to fuse biometrics at the score level. In addition, this paper constructs a virtual homologous dataset and conducts experiments on it. Experimental results demonstrate that the proposed method achieves an accuracy of 100% in both mAP and Rank1. To show the efficiency of the proposed method in practical applications, this work carries out more experiments utilizing real-world data. The results show that the proposed approach maintains a Rank1 accuracy of 99.2% on the million-scale database. It offers a novel approach to fusion at the score level.
Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques′ vulnerability to attack and poor performance in low-quality data. However, difficulties still exist in how to unify the meaning of heterogeneous scores more effectively. Aside from the matching scores themselves, the importance of the ranking information they include has been undervalued in previous studies. This study concentrates on matching scores and their ranking information and suggests the ranking partition collision (RPC) theory from the standpoint of the worth of scores. To meet both forensic and judicial needs, this paper proposes a method that employs a neural network to fuse biometrics at the score level. In addition, this paper constructs a virtual homologous dataset and conducts experiments on it. Experimental results demonstrate that the proposed method achieves an accuracy of 100% in both mAP and Rank1. To show the efficiency of the proposed method in practical applications, this work carries out more experiments utilizing real-world data. The results show that the proposed approach maintains a Rank1 accuracy of 99.2% on the million-scale database. It offers a novel approach to fusion at the score level.
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doi: 10.1007/s11633-022-1342-3
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Inspired by eagle eye mechanisms, the structure and information processing characteristics of the eagle′s visual system are used for the target capture task of an unmanned aerial vehicle (UAV) with a mechanical arm. In this paper, a novel eagle-eye inspired multi-camera sensor and a saliency detection method are proposed. A combined camera system is built by simulating the double fovea structure on the eagle retina. A saliency target detection method based on the eagle midbrain inhibition mechanism is proposed by measuring the static saliency information and dynamic features. Thus, salient targets can be accurately detected through the collaborative work between different cameras of the proposed multi-camera sensor. Experimental results show that the eagle-eye inspired visual system is able to continuously detect targets in outdoor scenes and that the proposed algorithm has a strong inhibitory effect on moving background interference.
Inspired by eagle eye mechanisms, the structure and information processing characteristics of the eagle′s visual system are used for the target capture task of an unmanned aerial vehicle (UAV) with a mechanical arm. In this paper, a novel eagle-eye inspired multi-camera sensor and a saliency detection method are proposed. A combined camera system is built by simulating the double fovea structure on the eagle retina. A saliency target detection method based on the eagle midbrain inhibition mechanism is proposed by measuring the static saliency information and dynamic features. Thus, salient targets can be accurately detected through the collaborative work between different cameras of the proposed multi-camera sensor. Experimental results show that the eagle-eye inspired visual system is able to continuously detect targets in outdoor scenes and that the proposed algorithm has a strong inhibitory effect on moving background interference.
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doi: 10.1007/s11633-022-1363-y
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For industrial processes, new scarce faults are usually judged by experts. The lack of instances for these faults causes a severe data imbalance problem for a diagnosis model and leads to low performance. In this article, a new diagnosis method with few-shot learning based on a class-rebalance strategy is proposed to handle the problem. The proposed method is designed to transform instances of the different faults into a feature embedding space. In this way, the fault features can be transformed into separate feature clusters. The fault representations are calculated as the centers of feature clusters. The representations of new faults can also be effectively calculated with few support instances. Therefore, fault diagnosis can be achieved by estimating feature similarity between instances and faults. A cluster loss function is designed to enhance the feature clustering performance. Also, a class-rebalance strategy with data augmentation is designed to imitate potential faults with different reasons and degrees of severity to improve the model′s generalizability. It improves the diagnosis performance of the proposed method. Simulations of fault diagnosis with the proposed method were performed on the Tennessee-Eastman benchmark. The proposed method achieved average diagnosis accuracies ranging from 81.8% to 94.7% for the eight selected faults for the simulation settings of support instances ranging from 3 to 50. The simulation results verify the effectiveness of the proposed method.
For industrial processes, new scarce faults are usually judged by experts. The lack of instances for these faults causes a severe data imbalance problem for a diagnosis model and leads to low performance. In this article, a new diagnosis method with few-shot learning based on a class-rebalance strategy is proposed to handle the problem. The proposed method is designed to transform instances of the different faults into a feature embedding space. In this way, the fault features can be transformed into separate feature clusters. The fault representations are calculated as the centers of feature clusters. The representations of new faults can also be effectively calculated with few support instances. Therefore, fault diagnosis can be achieved by estimating feature similarity between instances and faults. A cluster loss function is designed to enhance the feature clustering performance. Also, a class-rebalance strategy with data augmentation is designed to imitate potential faults with different reasons and degrees of severity to improve the model′s generalizability. It improves the diagnosis performance of the proposed method. Simulations of fault diagnosis with the proposed method were performed on the Tennessee-Eastman benchmark. The proposed method achieved average diagnosis accuracies ranging from 81.8% to 94.7% for the eight selected faults for the simulation settings of support instances ranging from 3 to 50. The simulation results verify the effectiveness of the proposed method.
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doi: 10.1007/s11633-022-1358-8
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Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.
Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.
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doi: 10.1007/s11633-022-1366-8
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Photoplethysmography (PPG) biometrics have received considerable attention. Although deep learning has achieved good performance for PPG biometrics, several challenges remain open: 1) How to effectively extract the feature fusion representation from time and frequency PPG signals. 2) How to effectively capture a series of PPG signal transition information. 3) How to extract time-varying information from one-dimensional time-frequency sequential data. To address these challenges, we propose a dual-domain and multiscale fusion deep neural network (DMFDNN) for PPG biometric recognition. The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics, which can learn the time-varying and multiscale discriminative features from the time and frequency domains. Meanwhile, we design a multiscale extraction module to capture transition information, which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information. In addition, the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics. Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
Photoplethysmography (PPG) biometrics have received considerable attention. Although deep learning has achieved good performance for PPG biometrics, several challenges remain open: 1) How to effectively extract the feature fusion representation from time and frequency PPG signals. 2) How to effectively capture a series of PPG signal transition information. 3) How to extract time-varying information from one-dimensional time-frequency sequential data. To address these challenges, we propose a dual-domain and multiscale fusion deep neural network (DMFDNN) for PPG biometric recognition. The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics, which can learn the time-varying and multiscale discriminative features from the time and frequency domains. Meanwhile, we design a multiscale extraction module to capture transition information, which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information. In addition, the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics. Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
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doi: 10.1007/s11633-022-1341-4
Abstract:
Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user′s finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.
Most finger vein authentication systems suffer from the problem of small sample size. However, the data augmentation can alleviate this problem to a certain extent but did not fundamentally solve the problem of category diversity. So the researchers resort to pre-training or multi-source data joint training methods, but these methods will lead to the problem of user privacy leakage. In view of the above issues, this paper proposes a federated learning-based finger vein authentication framework (FedFV) to solve the problem of small sample size and category diversity while protecting user privacy. Through training under FedFV, each client can share the knowledge learned from its user′s finger vein data with the federated client without causing template leaks. In addition, we further propose an efficient personalized federated aggregation algorithm, named federated weighted proportion reduction (FedWPR), to tackle the problem of non-independent identically distribution caused by client diversity, thus achieving the best performance for each client. To thoroughly evaluate the effectiveness of FedFV, comprehensive experiments are conducted on nine publicly available finger vein datasets. Experimental results show that FedFV can improve the performance of the finger vein authentication system without directly using other client data. To the best of our knowledge, FedFV is the first personalized federated finger vein authentication framework, which has some reference value for subsequent biometric privacy protection research.
, Available
online
doi: 10.1007/s11633-022-1345-0
Abstract:
Electrocardiogram (ECG) biometric recognition has gained considerable attention, and various methods have been proposed to facilitate its development. However, one limitation is that the diversity of ECG signals affects the recognition performance. To address this issue, in this paper, we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding. Firstly, we construct an enhanced correlation between the base feature and latent representation by using only one projection. Secondly, to fully exploit the semantic information, we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity. Furthermore, to solve the objective function, we propose an effective and efficient algorithm for optimization. Finally, extensive experiments are conducted on two benchmark datasets, and the experimental results show the effectiveness of our framework.
Electrocardiogram (ECG) biometric recognition has gained considerable attention, and various methods have been proposed to facilitate its development. However, one limitation is that the diversity of ECG signals affects the recognition performance. To address this issue, in this paper, we propose a novel ECG biometrics framework based on enhanced correlation and semantic-rich embedding. Firstly, we construct an enhanced correlation between the base feature and latent representation by using only one projection. Secondly, to fully exploit the semantic information, we take both the label and pairwise similarity into consideration to reduce the influence of ECG sample diversity. Furthermore, to solve the objective function, we propose an effective and efficient algorithm for optimization. Finally, extensive experiments are conducted on two benchmark datasets, and the experimental results show the effectiveness of our framework.
, Available
online
doi: 10.1007/s11633-022-1328-1
Abstract:
Adversarial example has been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD) but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family that covers many popular distributions (e.g., Laplacian, Gaussian, or uniform). Therefore, it is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier (MBF) coefficients, which can be easily estimated using responses. Finally, a support vector machine is trained as an adversarial detector leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.
Adversarial example has been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution (GGD) but with different parameters (i.e., shape factor, mean, and variance). GGD is a general distribution family that covers many popular distributions (e.g., Laplacian, Gaussian, or uniform). Therefore, it is more likely to approximate the intrinsic distributions of internal responses than any specific distribution. Besides, since the shape factor is more robust to different databases rather than the other two parameters, we propose to construct discriminative features via the shape factor for adversarial detection, employing the magnitude of Benford-Fourier (MBF) coefficients, which can be easily estimated using responses. Finally, a support vector machine is trained as an adversarial detector leveraging the MBF features. Extensive experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.
