Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Hamza, A. Ben"'
Autor:
Abdel-Sater, Raed, Hamza, A. Ben
Long-term time series forecasting in centralized environments poses unique challenges regarding data privacy, communication overhead, and scalability. To address these challenges, we propose FedTime, a federated large language model (LLM) tailored fo
Externí odkaz:
http://arxiv.org/abs/2407.20503
Although graph convolutional networks exhibit promising performance in 3D human pose estimation, their reliance on one-hop neighbors limits their ability to capture high-order dependencies among body joints, crucial for mitigating uncertainty arising
Externí odkaz:
http://arxiv.org/abs/2407.19077
Autor:
Zunair, Hasib, Hamza, A. Ben
Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typically available in supervised
Externí odkaz:
http://arxiv.org/abs/2407.17628
Autor:
Islam, Zaedul, Hamza, A. Ben
Publikováno v:
Journal of Visual Communication and Image Representation, 2024
Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi
Externí odkaz:
http://arxiv.org/abs/2405.03055
Road scene understanding is crucial in autonomous driving, enabling machines to perceive the visual environment. However, recent object detectors tailored for learning on datasets collected from certain geographical locations struggle to generalize a
Externí odkaz:
http://arxiv.org/abs/2401.07322
Autor:
Alshareet, Osama, Hamza, A. Ben
Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions to potential users based on their purchase or browsing history. However, personalized recommendations require considerab
Externí odkaz:
http://arxiv.org/abs/2312.03167
Autor:
Salim, Ibrahim, Hamza, A. Ben
While graph convolution based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurodevelopmental and neurodegenera
Externí odkaz:
http://arxiv.org/abs/2311.07370
Autor:
Zunair, Hasib, Hamza, A. Ben
Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small. While promising, existing multi-label image recognition models do not explicitly learn context-based representations, and
Externí odkaz:
http://arxiv.org/abs/2310.18517
Autor:
Hassan, Tanvir, Hamza, A. Ben
Graph convolutional networks and their variants have shown significant promise in 3D human pose estimation. Despite their success, most of these methods only consider spatial correlations between body joints and do not take into account temporal corr
Externí odkaz:
http://arxiv.org/abs/2308.15313
Autor:
Mesgaran, Mahsa, Hamza, A. Ben
Publikováno v:
Neural Computing and Applications, 2023
A key component of many graph neural networks (GNNs) is the pooling operation, which seeks to reduce the size of a graph while preserving important structural information. However, most existing graph pooling strategies rely on an assignment matrix o
Externí odkaz:
http://arxiv.org/abs/2308.07774