2 citations · 2 across the 3 of their papers we have counts for
5 papers
InstructMixup: Instruction-Guided Salient Patch Editing for Robust Data Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
In image and video technologies, data augmentation is widely used to improve the generalization of deep visual models, and mixup-based strategies that interpolate between samples h…
-FracMix: Label-Preserving Self-Saliency Mixup Augmentation
Khawar Islam, Arif Mahmood, Xin Jin +1
Data augmentation is known to improve generalization of deep visual models. Recent methods favor mixup strategies that generate interpolated samples to improve model performance. H…
Improving Generative Adversarial Network Generalization for Facial Expression Synthesis
Arbish Akram, Nazar Khan, Arif Mahmood
Facial expression synthesis aims to generate realistic facial expressions while preserving identity. Existing conditional generative adversarial networks (GANs) achieve excellent i…
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood +2
Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain…
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood +1
Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more random…