Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Laila Bashmal"'
Publikováno v:
Remote Sensing, Vol 16, Iss 9, p 1477 (2024)
In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking poten
Externí odkaz:
https://doaj.org/article/bbac3265f861430b919250d8f8f2e523
Autor:
Laila Bashmal, Yakoub Bazi, Farid Melgani, Riccardo Ricci, Mohamad M. Al Rahhal, Mansour Zuair
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3279-3293 (2023)
Visual question generation (VQG) is a fundamental task in vision-language understanding that aims to generate relevant questions about the given input image. In this article, we propose a paragraph-based VQG approach for generating intelligent questi
Externí odkaz:
https://doaj.org/article/b49cde89939c4b998ccfc39786ac1561
Autor:
Mohamad M. Al Rahhal, Yakoub Bazi, Norah A. Alsharif, Laila Bashmal, Naif Alajlan, Farid Melgani
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 9115-9126 (2022)
Cross-modal text-image retrieval in remote sensing (RS) provides a flexible retrieval experience for mining useful information from RS repositories. However, existing methods are designed to accept queries formulated in the English language only, whi
Externí odkaz:
https://doaj.org/article/6f63226a433d4cf98d007509067e18aa
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 2139 (2023)
In this paper, we introduce the CapERA dataset, which upgrades the Event Recognition in Aerial Videos (ERA) dataset to aerial video captioning. The newly proposed dataset aims to advance visual–language-understanding tasks for UAV videos by providi
Externí odkaz:
https://doaj.org/article/e9af47099bf741cebe2c5b9f99ee938b
Publikováno v:
Bioengineering, Vol 10, Iss 3, p 380 (2023)
In the clinical and healthcare domains, medical images play a critical role. A mature medical visual question answering system (VQA) can improve diagnosis by answering clinical questions presented with a medical image. Despite its enormous potential
Externí odkaz:
https://doaj.org/article/043e97016bc343d78b593a5b451e4eb3
Publikováno v:
Applied Sciences, Vol 11, Iss 9, p 3974 (2021)
In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation
Externí odkaz:
https://doaj.org/article/d8c45fb6d39f40d38e6ad4400a21d159
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 516 (2021)
In this paper, we propose a remote-sensing scene-classification method based on vision transformers. These types of networks, which are now recognized as state-of-the-art models in natural language processing, do not rely on convolution layers as in
Externí odkaz:
https://doaj.org/article/aa8eacb888bb4e66a99f84c0a67ceba6
Autor:
Laila Bashmal, Yakoub Bazi, Haikel AlHichri, Mohamad M. AlRahhal, Nassim Ammour, Naif Alajlan
Publikováno v:
Remote Sensing, Vol 10, Iss 2, p 351 (2018)
In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and
Externí odkaz:
https://doaj.org/article/1e9b8e2c41214b118f2dbbf593775637
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Publikováno v:
IGARSS
In this paper, we present a scene classification method based on vision transformers. These types of networks, which are now the standard models in natural language processing (NLP) do not rely on convolution block as in convolutional neural networks