activity
20192022
most citedCFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors

Karim Guirguis, Mohamed Abdelsamad, George Eskandar +4

Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learn…

cs.CV20221 cited

CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

Karim Guirguis, Ahmed Hendawy, George Eskandar +3

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-…

cs.CV20221 cited

An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions

George Eskandar, Robert A. Marsden, Pavithran Pandiyan +3

Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that…

eess.AS2020

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

Karim Guirguis, Christoph Schorn, Andre Guntoro +2

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars. Extensive research has been carried out concerning…

eess.AS2019

AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks

Sherif Abdulatif, Karim Armanious, Karim Guirguis +2

Automatic speech recognition (ASR) systems are of vital importance nowadays in commonplace tasks such as speech-to-text processing and language translation. This created the need f…