Zobrazeno 1 - 10
of 498
pro vyhledávání: '"Nasrabadi, Nasser M."'
Autor:
Saadabadi, Mohammad Saeed Ebrahimi, Malakshan, Sahar Rahimi, Hosseini, Seyed Rasoul, Nasrabadi, Nasser M.
While deep face recognition models have demonstrated remarkable performance, they often struggle on the inputs from domains beyond their training data. Recent attempts aim to expand the training set by relying on computationally expensive and inheren
Externí odkaz:
http://arxiv.org/abs/2408.07642
Autor:
Rahman, Chowdhury Mohammad Abid, Bhandari, Ghadendra, Nasrabadi, Nasser M, Romero, Aldo H., Gyawali, Prashnna K.
Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced te
Externí odkaz:
http://arxiv.org/abs/2407.18847
The challenge of deblurring fingerphoto images, or generating a sharp fingerphoto from a given blurry one, is a significant problem in the realm of computer vision. To address this problem, we propose a fingerphoto deblurring architecture referred to
Externí odkaz:
http://arxiv.org/abs/2407.15964
Autor:
Saadabadi, Mohammad Saeed Ebrahimi, Malakshan, Sahar Rahimi, Dabouei, Ali, Nasrabadi, Nasser M.
Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specifi
Externí odkaz:
http://arxiv.org/abs/2407.14972
Autor:
Khoshkhahtinat, Atefeh, Zafari, Ali, Mehta, Piyush M., Nasrabadi, Nasser M., Thompson, Barbara J., Kirk, Michael S. F., da Silva, Daniel
NASA's Solar Dynamics Observatory (SDO) mission collects extensive data to monitor the Sun's daily activity. In the realm of space mission design, data compression plays a crucial role in addressing the challenges posed by limited telemetry rates. Th
Externí odkaz:
http://arxiv.org/abs/2407.15730
While replacing Gaussian decoders with a conditional diffusion model enhances the perceptual quality of reconstructions in neural image compression, their lack of inductive bias for image data restricts their ability to achieve state-of-the-art perce
Externí odkaz:
http://arxiv.org/abs/2403.16258
Annotating automatic target recognition (ATR) is a highly challenging task, primarily due to the unavailability of labeled data in the target domain. Hence, it is essential to construct an optimal target domain classifier by utilizing the labeled inf
Externí odkaz:
http://arxiv.org/abs/2401.12340
Although face recognition (FR) has achieved great success in recent years, it is still challenging to accurately recognize faces in low-quality images due to the obscured facial details. Nevertheless, it is often feasible to make predictions about sp
Externí odkaz:
http://arxiv.org/abs/2401.03037
Autor:
Zafari, Ali, Khoshkhahtinat, Atefeh, Grajeda, Jeremy A., Mehta, Piyush M., Nasrabadi, Nasser M., Boucheron, Laura E., Thompson, Barbara J., Kirk, Michael S. F., da Silva, Daniel
Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to m
Externí odkaz:
http://arxiv.org/abs/2311.02855
Despite the advances in the field of Face Recognition (FR), the precision of these methods is not yet sufficient. To improve the FR performance, this paper proposes a technique to aggregate the outputs of two state-of-the-art (SOTA) deep FR models, n
Externí odkaz:
http://arxiv.org/abs/2309.13137