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20162024
most citedEnd-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies

70 citations · 109 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.CV2023

The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses

Mahmoud Ahmed, Omer Moussa, Ismail Shaheen +5

One of the major challenges in training deep neural networks for text-to-image generation is the significant linguistic discrepancy between ground-truth captions of each image in m…

cs.CV2023★ 1 cited

RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution

Marwah Sulaiman, Zahraa Shehabeldin, Israa Fahmy +5

Recently, video super resolution (VSR) has become a very impactful task in the area of Computer Vision due to its various applications. In this paper, we propose Recurrent Back-Pro…

cs.CV2021★ 3 cited

An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

Amr S. Mohamed, Ali Abdelkader, Mohamed Anany +5

LiDARs and cameras are the two main sensors that are planned to be included in many announced autonomous vehicles prototypes. Each of the two provides a unique form of data from a…

cs.CV2021★ 2 cited

Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading

Shahd Elashmawy, Marian Ramsis, Hesham M. Eraqi +4

Despite the advancement in the domain of audio and audio-visual speech recognition, visual speech recognition systems are still quite under-explored due to the visual ambiguity of…

cs.CV2019

Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

Hesham M. Eraqi, Yehya Abouelnaga, Mohamed H. Saad +1

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few ye…

cs.CV2017

Real-time Distracted Driver Posture Classification

Yehya Abouelnaga, Hesham M. Eraqi, Mohamed N. Moustafa

In this paper, we present a new dataset for "distracted driver" posture estimation. In addition, we propose a novel system that achieves 95.98% driving posture estimation classific…