Accurate road extraction from remote sensing images is a critical step for many applications, including navigation, environmental monitoring, and disaster response. In remote sensing images, roads appear as thin curvi...
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The paper presents a framework for the detection of mass-like lesions in 3D digital breast tomosynthesis. It consists of several steps, including pre and post-processing, and a main detection block based on a Faster R...
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The use of technology in agriculture has grown imperative. To provide the expanding population's needs for food, agricultural productivity should rise. computervision technology has been used to solve the difficu...
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Due to privacy concerns, obtaining large datasets is challenging in medical image analysis, especially with 3D modalities like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing generative models,...
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Deep convolution neural networks are going deeper and ***-ever,the complexity of models is prone to overfitting in ***,one of the crucial tricks,prevents units from co-adapting too much by randomly drop-ping neurons d...
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Deep convolution neural networks are going deeper and ***-ever,the complexity of models is prone to overfitting in ***,one of the crucial tricks,prevents units from co-adapting too much by randomly drop-ping neurons during *** effectively improves the performance of deep net-works but ignores the importance of the differences between *** optimize this issue,this paper presents a new dropout method called guided dropout,which selects the neurons to switch off according to the differences between the convo-lution kernel and preserves the informative *** uses an unsupervised clus-tering algorithm to cluster similar neurons in each hidden layer,and dropout uses a certain probability within each *** this would preserve the hidden layer neurons with different roles while maintaining the model’s scarcity and gen-eralization,which effectively improves the role of the hidden layer neurons in learning the *** evaluated our approach compared with two standard dropout networks on three well-established public object detection *** results on multiple datasets show that the method proposed in this paper has been improved on false positives,precision-recall curve and average precision without increasing the amount of *** can be seen that the increased performance of guided dropout is thanks to shallow learning in the *** concept of guided dropout would be beneficial to the other vision tasks.
Index-to-palm interaction plays a crucial role in Mixed Reality(MR) interactions. However, achieving a satisfactory inter-hand interaction experience is challenging with existing vision-based hand tracking technologie...
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Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a sign...
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Recovering a high-level representation of geometric data is a fundamental goal in geometric modeling and computer graphics. In this paper, we introduce a data-driven approach to computing the spectrum of the Laplace-B...
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Recovering a high-level representation of geometric data is a fundamental goal in geometric modeling and computer graphics. In this paper, we introduce a data-driven approach to computing the spectrum of the Laplace-Beltrami operator of triangle meshes using graph convolutional networks. Specifically, we train graph convolutional networks on a large-scale dataset of synthetically generated triangle meshes, encoded with geometric data consisting of Voronoi areas, normalized edge lengths, and the Gauss map, to infer eigenvalues of 3D shapes. We attempt to address the ability of graph neural networks to capture global shape descriptors–including spectral information–that were previously inaccessible using existing methods from computervision, and our paper exhibits promising signals suggesting that Laplace-Beltrami eigenvalues on discrete surfaces can be learned. Additionally, we perform ablation studies showing the addition of geometric data leads to improved accuracy. Copyright 2024 by the author(s).
Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the blac...
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ISBN:
(纸本)9798350329964
Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.
This paper presents the approach of the iHealthChile-1 team for the shared task of Large-Scale Radiology Report Generation at the BioNLP workshop, inspired by progress in large multimodal models for processing images ...
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