Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Ksantini, Riadh"'
Image segmentation is the foundation of several computer vision tasks, where pixel-wise knowledge is a prerequisite for achieving the desired target. Deep learning has shown promising performance in supervised image segmentation. However, supervised
Externí odkaz:
http://arxiv.org/abs/2403.11266
Publikováno v:
Pattern Recognition 2023
Variational Graph Auto-Encoders (VGAEs) have been widely used to solve the node clustering task. However, the state-of-the-art methods have numerous challenges. First, existing VGAEs do not account for the discrepancy between the inference and genera
Externí odkaz:
http://arxiv.org/abs/2312.16830
We devise a graph attention network-based approach for learning a scene triangle mesh representation in order to estimate an image camera position in a dynamic environment. Previous approaches built a scene-dependent model that explicitly or implicit
Externí odkaz:
http://arxiv.org/abs/2209.15056
Publikováno v:
In Image and Vision Computing October 2024 150
Publikováno v:
In Computers & Security September 2024 144
Publikováno v:
In Neural Networks January 2025 181
Most recent graph clustering methods have resorted to Graph Auto-Encoders (GAEs) to perform joint clustering and embedding learning. However, two critical issues have been overlooked. First, the accumulative error, inflicted by learning with noisy cl
Externí odkaz:
http://arxiv.org/abs/2107.08562
Publikováno v:
In Pattern Recognition May 2024 149
Publikováno v:
Advances in Visual Computing 2020. Lecture Notes in Computer Science, vol 12509. Springer
During the last years, deep learning trackers achieved stimulating results while bringing interesting ideas to solve the tracking problem. This progress is mainly due to the use of learned deep features obtained by training deep convolutional neural
Externí odkaz:
http://arxiv.org/abs/2012.12784
Clustering using deep autoencoders has been thoroughly investigated in recent years. Current approaches rely on simultaneously learning embedded features and clustering the data points in the latent space. Although numerous deep clustering approaches
Externí odkaz:
http://arxiv.org/abs/1909.11832