Zobrazeno 1 - 10
of 5 390
pro vyhledávání: '"Fiaz, A."'
Autor:
Ramzan, Muhammad Umer, Khaddim, Wahab, Rana, Muhammad Ehsan, Ali, Usman, Ali, Manohar, Hassan, Fiaz ul, Mehmood, Fatima
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become
Externí odkaz:
http://arxiv.org/abs/2411.19690
Autor:
Imam, Mohamed Fazli, Marew, Rufael Fedaku, Hassan, Jameel, Fiaz, Mustansar, Aji, Alham Fikri, Cholakkal, Hisham
In the era of foundation models, CLIP has emerged as a powerful tool for aligning text and visual modalities into a common embedding space. However, the alignment objective used to train CLIP often results in subpar visual features for fine-grained t
Externí odkaz:
http://arxiv.org/abs/2411.19346
Automated waste recycling aims to efficiently separate the recyclable objects from the waste by employing vision-based systems. However, the presence of varying shaped objects having different material types makes it a challenging problem, especially
Externí odkaz:
http://arxiv.org/abs/2410.24139
Autor:
Kareem, Daniya Najiha Abdul, Fiaz, Mustansar, Novershtern, Noa, Hanna, Jacob, Cholakkal, Hisham
Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encod
Externí odkaz:
http://arxiv.org/abs/2410.15360
Autor:
Paul, Lennart, Urrea-Quintero, Jorge-Humberto, Fiaz, Umer, Hussein, Ali, Yaghi, Hazem, Wessels, Henning, Römer, Ulrich, Stahlmann, Joachim
This work introduces a surrogate modeling approach for an emplacement drift of a deep geological repository based on Gaussian Processes (GPs). The surrogate model is used as a substitute for the high-fidelity mechanical model in many-query scenarios,
Externí odkaz:
http://arxiv.org/abs/2409.02576
Autor:
Khan, Asifullah, Sohail, Anabia, Fiaz, Mustansar, Hassan, Mehdi, Afridi, Tariq Habib, Marwat, Sibghat Ullah, Munir, Farzeen, Ali, Safdar, Naseem, Hannan, Zaheer, Muhammad Zaigham, Ali, Kamran, Sultana, Tangina, Tanoli, Ziaurrehman, Akhter, Naeem
Deep supervised learning models require high volume of labeled data to attain sufficiently good results. Although, the practice of gathering and annotating such big data is costly and laborious. Recently, the application of self supervised learning (
Externí odkaz:
http://arxiv.org/abs/2408.17059
Existing deep learning approaches leave out the semantic cues that are crucial in semantic segmentation present in complex scenarios including cluttered backgrounds and translucent objects, etc. To handle these challenges, we propose a feature amplif
Externí odkaz:
http://arxiv.org/abs/2407.09379
Accurate segmentation of medical images is crucial for diagnostic purposes, including cell segmentation, tumor identification, and organ localization. Traditional convolutional neural network (CNN)-based approaches struggled to achieve precise segmen
Externí odkaz:
http://arxiv.org/abs/2406.17471
Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majorit
Externí odkaz:
http://arxiv.org/abs/2405.15453
Change detection (CD) is a fundamental task in remote sensing (RS) which aims to detect the semantic changes between the same geographical regions at different time stamps. Existing convolutional neural networks (CNNs) based approaches often struggle
Externí odkaz:
http://arxiv.org/abs/2404.17565