Search Results for author: Karim Guirguis

Found 12 papers, 2 papers with code

Towards Pragmatic Semantic Image Synthesis for Urban Scenes

1 code implementation16 May 2023 George Eskandar, Diandian Guo, Karim Guirguis, Bin Yang

Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches.

Autonomous Driving Image Generation

Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes

no code implementations16 May 2023 George Eskandar, Youssef Farag, Tarun Yenamandra, Daniel Cremers, Karim Guirguis, Bin Yang

Moreover, we employ an unsupervised latent exploration algorithm in the $\mathcal{S}$-space of the generator and show that it is more efficient than the conventional $\mathcal{W}^{+}$-space in controlling the image content.

Autonomous Driving Disentanglement +2

NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging

no code implementations CVPR 2023 Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser, Bin Yang, Juergen Beyerer

Our contribution is three-fold: (1) we design a standalone lightweight generator with (2) class-wise heads (3) to generate and replay diverse instance-level base features to the RoI head while finetuning on the novel data.

Data-free Knowledge Distillation Few-Shot Object Detection +2

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

no code implementations11 Oct 2022 Karim Guirguis, Mohamed Abdelsamad, George Eskandar, Ahmed Hendawy, Matthias Kayser, Bin Yang, Juergen Beyerer

We make the observation that the large gap in performance between two-stage and one-stage FSODs are mainly due to their weak discriminability, which is explained by a small post-fusion receptive field and a small number of foreground samples in the loss function.

Few-Shot Object Detection object-detection

Few-Shot Object Detection in Unseen Domains

no code implementations11 Apr 2022 Karim Guirguis, George Eskandar, Matthias Kayser, Bin Yang, Juergen Beyerer

First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes.

Domain Generalization Few-Shot Object Detection +2

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

no code implementations11 Apr 2022 Karim Guirguis, Ahmed Hendawy, George Eskandar, Mohamed Abdelsamad, Matthias Kayser, Juergen Beyerer

In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity.

Continual Learning Few-Shot Object Detection +1

Investigating Cross-Domain Losses for Speech Enhancement

no code implementations20 Oct 2020 Sherif Abdulatif, Karim Armanious, Jayasankar T. Sajeev, Karim Guirguis, Bin Yang

Recent years have seen a surge in the number of available frameworks for speech enhancement (SE) and recognition.

Speech Enhancement

AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks

no code implementations21 Oct 2019 Sherif Abdulatif, Karim Armanious, Karim Guirguis, Jayasankar T. Sajeev, Bin Yang

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

Automatic Speech Recognition Automatic Speech Recognition (ASR) +6

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