Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...
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Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen *** the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic ...
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Brain tumors can be life-threatening. Early detection and accurate determination of the type and location of brain tumors are crucial for intervening in the condition of brain tumor patients and saving lives. In areas...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of acc...
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In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE
With the rapid development of laser technology and computertechnology, terrain data is usually obtained by airborne LiDAR detection. In terrain data processing, bilateral filtering method is widely used, but the filt...
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In order to improve the safety of nighttime car driving, this paper proposes a lightweight image fusion network based on depth-separable self-encoder combined with attention mechanism for the problems of insufficient ...
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In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban...
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In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield *** combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on *** to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases ***,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network *** results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target *** research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.
In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity ***,due to static pressure,temperature difference and other factors,random disturbances may often frequently ...
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In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity ***,due to static pressure,temperature difference and other factors,random disturbances may often frequently in seawater *** impact of such disturbances on data processing results is a topic of theoretical *** seawater sound velocity is a difficult physical quantity to measure,there is a need for a method that can generate models conforming to seawater *** article will combine the Munk model and Perlin noise to propose a two-dimensional dynamic seawater sound velocity model generation method,a method that can generate a dynamic,continuous,random seawater sound velocity model with some regularity at large ***,the paper discusses the influence of the inhomogeneity characteristics of seawater on wave field propagation and *** results show that the seawater sound velocity model with random disturbance will have a significant influence on the wave field simulation and imaging results.
Polyoxometalates(POMs)are classified as solid superacids which can exhibit notable proton conductivity,making them a promising functional inorganic filler for enhancing the proton conductivity of proton exchange membr...
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Polyoxometalates(POMs)are classified as solid superacids which can exhibit notable proton conductivity,making them a promising functional inorganic filler for enhancing the proton conductivity of proton exchange membranes(PEMs).In this study,a series of hybrid membranes were obtained by molecular-level hybridization of Weakley-type POM Na_(7)H_(2)LaW_(10)O_(36)(LaW_(10))clusters into sulfonated poly(aryl ether ketone sulfone)(SPAEKS).All hybrid membranes exhibited greater proton conductivity than the pristine membrane in the 30–80℃temperature *** the doping amount of LaW 10 reached 7 wt.%,the proton conductivity of M-LaW 10^(-7)achieved 64 mS·cm^(−1)at 80℃.Lanthanide ions'high coordination number property and variable coordination environment can aid to attract more water molecules from the *** 10 and these bound water can construct denser hydrogen bonds with–SO_(3)H of *** intensive hydrogen bonds will facilitate the constitution of more continuous proton transport channels,and improve the proton conductivity of the hybrid *** work off ers a fresh approach to using POMs containing rare-earth components in PEMs.
Early diagnosis of brain tumors is of great significance to patients, families, and society. Therefore, it is necessary to develop more accurate, fast, and reliable diagnostic methods for brain tumors. Deep learning b...
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