Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in ...
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Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection. Although Self-Training (ST) based cross-domain 3D detection methods with the assistance of pseudo-labeling techniques have achieved remarkable progress, they still face the issue of low-quality pseudo-labels when there are significant domain disparities due to the absence of a process for feature distribution alignment. While Adversarial Learning (AL) based methods can effectively align the feature distributions of the source and target domains, the inability to obtain labels in the target domain forces the adoption of asymmetric optimization losses, resulting in a challenging issue of source domain bias. To overcome these limitations, we propose a novel unsupervised domain adaptation framework for 3D object detection via collaborating ST and AL, dubbed as STAL3D, unleashing the complementary advantages of pseudo labels and feature distribution alignment. Additionally, a Background Suppression Adversarial Learning (BS-AL) module and a Scale Filtering Module (SFM) are designed tailored for 3D cross-domain scenes, effectively alleviating the issues of the large proportion of background interference and source domain size bias. Our STAL3D achieves state-of-the-art performance on multiple cross-domain tasks and even surpasses the Oracle results on Waymo $\rightarrow$ KITTI and Waymo $\rightarrow$ KITTI-rain. IEEE
Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetatio...
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Preserving biodiversity and maintaining ecological balance is essential in current environmental *** is challenging to determine vegetation using traditional map classification *** primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral *** is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed *** proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation *** architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation *** novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight *** system considers detailing feature areas to improve classification accuracy and reduce processing *** proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 *** training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM *** system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.
Low-light image enhancement (LLIE) in Raw space has posed a challenge in the field of image processing and computational photography. Different from image processing in sRGB space, Raw images store more image informat...
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Unmanned Surface Vehicles (USVs) are pivotal in diverse marine operations, including search and rescue, environmental monitoring, and maritime security. As their application grows, coordinating multiple USVs for colla...
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The analysis of artery and vein differences in Optical Coherence Tomography Angiography (OCTA) is of great significance for diagnosing various eye diseases and systemic diseases (such as diabetic retinopathy, hyperten...
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With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery ...
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With the popularity of online learning in educational settings, knowledge tracing(KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations,we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation *** model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed Graph CA method compared with several state-of-the-art baselines.
Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves mo...
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3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking mo...
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Most of the devastating cyber-attacks are caused by insiders with access privileges inside an organization. The main reason of insider attacks being more effective is that they don't have many security barriers be...
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Autism spectrum disease (ASD) is a neuro developmental illness that is both complicated and degenerative. A majority of known approaches use autism detection observation schedule (ADOS), pattern recognition, etc. to d...
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