The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information.Due to the higher similarity in appearance between vehicles compare...The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information.Due to the higher similarity in appearance between vehicles compared to pedestrians,pseudo-labels generated through clustering are ineffective in mitigating the impact of noise,and the feature distance between inter-class and intra-class has not been adequately improved.To address the aforementioned issues,we design a dual contrastive learning method based on knowledge distillation.During each iteration,we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories.By conducting contrastive learning between the two student models,we extract more discernible vehicle identity cues to improve the problem of imbalanced data distribution.Subsequently,we propose a context-aware pseudo label refinement strategy that leverages contextual features by progressively associating granularity information from different bottleneck blocks.To produce more trustworthy pseudo-labels and lessen noise interference during the clustering process,the context-aware scores are obtained by calculating the similarity between global features and contextual ones,which are subsequently added to the pseudo-label encoding process.The proposed method has achieved excellent performance in overcoming label noise and optimizing data distribution through extensive experimental results on publicly available datasets.展开更多
Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) ...Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) in the resting state and the right finger taping state.Methods 3 D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3 D T1-FSPGR) sequence were applied to eight healthy subjects twice at the same time each day for one week interval. ASL data acquisition was performed with post-labeling delay time(PLD) 1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively. CBF mapping was calculated and CBF value of both the gray matter(GM) and white matter(WM) was automatically extracted. The reliability was evaluated using the intraclass correlation coefficient(ICC) and Bland and Altman plot.Results ICC of the GM(0.84) and WM(0.92) was lower at PLD 1.5 seconds than that(GM, 0.88; WM, 0.94) at PLD 2.0 seconds in the resting state, and ICC of GM(0.88) was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds. ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan, and ICC of the GM and WM was 0.83 and 0.79 at the second scan, respectively.Conclusion This work demonstrated that 3 D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.展开更多
随着网络信息规模的迅速增长,网络结构和数据流日益复杂,如何有效识别这些海量数据中的异常行为已成为网络安全领域的重要挑战。目前,基于深度学习的异常行为检测方法主要针对静态网络,并且依赖标注数据,忽略了大量未标记数据的潜在价...随着网络信息规模的迅速增长,网络结构和数据流日益复杂,如何有效识别这些海量数据中的异常行为已成为网络安全领域的重要挑战。目前,基于深度学习的异常行为检测方法主要针对静态网络,并且依赖标注数据,忽略了大量未标记数据的潜在价值。因此,提出一种基于动态图嵌入与对比学习的网络异常行为检测方法(network anomaly behavior detection method based on Dynamic Graph embedding and Contrastive Learning,DGCL)。该方法融合全局空间特征、局部结构特征和时间动态特征,利用Transformer生成高质量的节点表示,结合伪标签和对比学习策略提升检测性能。在Wikipedia、Reddit和Mooc这3个数据集上进行实验验证,结果表明:DGCL分别达到了87.89%、70.38%和70.11%的AUC值,相比其他同类方法,DGCL在动态网络异常检测中表现出更好的性能。展开更多
In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from ...In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.62461037,62076117 and 62166026the Jiangxi Provincial Natural Science Foundation under Grant Nos.20224BAB212011,20232BAB202051,20232BAB212008 and 20242BAB25078the Jiangxi Provincial Key Laboratory of Virtual Reality under Grant No.2024SSY03151.
文摘The unsupervised vehicle re-identification task aims at identifying specific vehicles in surveillance videos without utilizing annotation information.Due to the higher similarity in appearance between vehicles compared to pedestrians,pseudo-labels generated through clustering are ineffective in mitigating the impact of noise,and the feature distance between inter-class and intra-class has not been adequately improved.To address the aforementioned issues,we design a dual contrastive learning method based on knowledge distillation.During each iteration,we utilize a teacher model to randomly partition the entire dataset into two sub-domains based on clustering pseudo-label categories.By conducting contrastive learning between the two student models,we extract more discernible vehicle identity cues to improve the problem of imbalanced data distribution.Subsequently,we propose a context-aware pseudo label refinement strategy that leverages contextual features by progressively associating granularity information from different bottleneck blocks.To produce more trustworthy pseudo-labels and lessen noise interference during the clustering process,the context-aware scores are obtained by calculating the similarity between global features and contextual ones,which are subsequently added to the pseudo-label encoding process.The proposed method has achieved excellent performance in overcoming label noise and optimizing data distribution through extensive experimental results on publicly available datasets.
基金Supported by the Foundation for Medical and Health Sci&Tech Innovation Project of Sanya(2016YW37)the Special Financial Grant from China Postdoctoral Science Foundation(2014T70960)
文摘Objective To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3 D pc-ASL) in measuring cerebral blood flow(CBF) with different post-labeling delay time(PLD) in the resting state and the right finger taping state.Methods 3 D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3 D T1-FSPGR) sequence were applied to eight healthy subjects twice at the same time each day for one week interval. ASL data acquisition was performed with post-labeling delay time(PLD) 1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively. CBF mapping was calculated and CBF value of both the gray matter(GM) and white matter(WM) was automatically extracted. The reliability was evaluated using the intraclass correlation coefficient(ICC) and Bland and Altman plot.Results ICC of the GM(0.84) and WM(0.92) was lower at PLD 1.5 seconds than that(GM, 0.88; WM, 0.94) at PLD 2.0 seconds in the resting state, and ICC of GM(0.88) was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds. ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan, and ICC of the GM and WM was 0.83 and 0.79 at the second scan, respectively.Conclusion This work demonstrated that 3 D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.
文摘随着网络信息规模的迅速增长,网络结构和数据流日益复杂,如何有效识别这些海量数据中的异常行为已成为网络安全领域的重要挑战。目前,基于深度学习的异常行为检测方法主要针对静态网络,并且依赖标注数据,忽略了大量未标记数据的潜在价值。因此,提出一种基于动态图嵌入与对比学习的网络异常行为检测方法(network anomaly behavior detection method based on Dynamic Graph embedding and Contrastive Learning,DGCL)。该方法融合全局空间特征、局部结构特征和时间动态特征,利用Transformer生成高质量的节点表示,结合伪标签和对比学习策略提升检测性能。在Wikipedia、Reddit和Mooc这3个数据集上进行实验验证,结果表明:DGCL分别达到了87.89%、70.38%和70.11%的AUC值,相比其他同类方法,DGCL在动态网络异常检测中表现出更好的性能。
文摘In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.
