In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the...In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion.By comparing the measured distance between two pedestrians,pedestrian re-recognition is accomplished.Firstly,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as possible.The method consists of two branches:global feature extraction and local feature extraction.Then the global feature and local feature are fused.The fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.展开更多
Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.De...Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.Despite the improvement in performance of the cycle-consistent generative adversarial network(CycleGAN)due to the well-paired CT images shortage,there is still a need to further reduce image noise while retaining detailed features.Inspired by the residual encoder–decoder convolutional neural network(RED-CNN)and U-Net,we propose a novel unsupervised model using CycleGAN for LDCT imaging,which injects a two-sided network into selective kernel networks(SK-NET)to adaptively select features,and uses the patchGAN discriminator to generate CT images with more detail maintenance,aided by added perceptual loss.Based on patch-based training,the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset.The main advantages of our method lie in noise suppression and edge preservation.展开更多
The study on ship wakes of synthetic aperture radar(SAR)images holds great importance in detecting ship targets in the ocean.In this study,we focus on the issues of low quantity and insufficient diversity in ship wake...The study on ship wakes of synthetic aperture radar(SAR)images holds great importance in detecting ship targets in the ocean.In this study,we focus on the issues of low quantity and insufficient diversity in ship wakes of SAR images,and propose a method of data augmentation of ship wakes in SAR images based on the improved cycle-consistent generative adversarial network(CycleGAN).The improvement measures mainly include two aspects:First,to enhance the quality of the generated images and guarantee a stable training process of the model,the least-squares loss is employed as the adversarial loss function;Second,the decoder of the generator is augmented with the convolutional block attention module(CBAM)to address the issue of missing details in the generated ship wakes of SAR images at the microscopic level.The experiment findings indicate that the improved CycleGAN model generates clearer ship wakes of SAR images,and outperforms the traditional CycleGAN models in both subjective and objective aspects.展开更多
Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosen...Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.展开更多
Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer i...Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.展开更多
Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targ...Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targets limit the availability of SSS data,posing challenges for Automatic Target Recognition(ATR)research.This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks(CycleGAN)to augment SSS images of key subsea objects,such as shipwrecks,aircraft,and drowning victims.The process begins by constructing 3D models to generate rendered images with realistic shadows frommultiple angles.To enhance image quality,a shadowextractor and shadow region loss function are introduced to ensure consistent shadowrepresentation.Additionally,amulti-resolution learning structure enables effective training,even with limited data availability.The experimental results show that the generated data improved object detection accuracy when they were used for training and demonstrated the ability to generate clear shadow and background regions with stability.展开更多
文摘In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect decline.In order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature fusion.By comparing the measured distance between two pedestrians,pedestrian re-recognition is accomplished.Firstly,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as possible.The method consists of two branches:global feature extraction and local feature extraction.Then the global feature and local feature are fused.The fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.
基金funded by the National Natural Science Foundation of China(Grants No.61871277 and 61671312)in part by the Project of State Administration of Traditional Chinese Medicine of Sichuan(Grant No.2021MS012).
文摘Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.Despite the improvement in performance of the cycle-consistent generative adversarial network(CycleGAN)due to the well-paired CT images shortage,there is still a need to further reduce image noise while retaining detailed features.Inspired by the residual encoder–decoder convolutional neural network(RED-CNN)and U-Net,we propose a novel unsupervised model using CycleGAN for LDCT imaging,which injects a two-sided network into selective kernel networks(SK-NET)to adaptively select features,and uses the patchGAN discriminator to generate CT images with more detail maintenance,aided by added perceptual loss.Based on patch-based training,the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset.The main advantages of our method lie in noise suppression and edge preservation.
基金Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2021-5)Aeronautical Science Foundation of China(No.20220001057001)。
文摘The study on ship wakes of synthetic aperture radar(SAR)images holds great importance in detecting ship targets in the ocean.In this study,we focus on the issues of low quantity and insufficient diversity in ship wakes of SAR images,and propose a method of data augmentation of ship wakes in SAR images based on the improved cycle-consistent generative adversarial network(CycleGAN).The improvement measures mainly include two aspects:First,to enhance the quality of the generated images and guarantee a stable training process of the model,the least-squares loss is employed as the adversarial loss function;Second,the decoder of the generator is augmented with the convolutional block attention module(CBAM)to address the issue of missing details in the generated ship wakes of SAR images at the microscopic level.The experiment findings indicate that the improved CycleGAN model generates clearer ship wakes of SAR images,and outperforms the traditional CycleGAN models in both subjective and objective aspects.
基金supported in part by the US National Science Foundation(NSF)under Grants ECCS-1923163 and CNS-2107190through the Wireless Engineering Research and Education Center at Auburn University.
文摘Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.
基金National Natural Science Foundation of China(61976209,62020106015,U21A20388)in part by the CAS International Collaboration Key Project(173211KYSB20190024)in part by the Strategic Priority Research Program of CAS(XDB32040000)。
文摘Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024-00334159)the Korea Institute of Ocean Science and Technology(KIOST)project entitled“Development of Maritime Domain Awareness Technology for Sea Power Enhancement”(PEA0332).
文摘Side-scan sonar(SSS)is essential for acquiring high-resolution seafloor images over large areas,facilitat-ing the identification of subsea objects.However,military security restrictions and the scarcity of subsea targets limit the availability of SSS data,posing challenges for Automatic Target Recognition(ATR)research.This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks(CycleGAN)to augment SSS images of key subsea objects,such as shipwrecks,aircraft,and drowning victims.The process begins by constructing 3D models to generate rendered images with realistic shadows frommultiple angles.To enhance image quality,a shadowextractor and shadow region loss function are introduced to ensure consistent shadowrepresentation.Additionally,amulti-resolution learning structure enables effective training,even with limited data availability.The experimental results show that the generated data improved object detection accuracy when they were used for training and demonstrated the ability to generate clear shadow and background regions with stability.
