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20212025
most citedExploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning

8 citations · 22 across the 17 of their papers we have counts for

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Showing 2023 · cs.CVShow all

5 papers · 2 filters

cs.CV2023

Free-Editor: Zero-shot Text-driven 3D Scene Editing

Nazmul Karim, Hasan Iqbal, Umar Khalid +2

Text-to-Image (T2I) diffusion models have recently gained traction for their versatility and user-friendliness in 2D content generation and editing. However, training a diffusion m…

cs.CV2023

LatentEditor: Text Driven Local Editing of 3D Scenes

Umar Khalid, Hasan Iqbal, Nazmul Karim +2

While neural fields have made significant strides in view synthesis and scene reconstruction, editing them poses a formidable challenge due to their implicit encoding of geometry a…

cs.CV2023★ 1 cited

CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction

Umar Khalid, Hasan Iqbal, Saeed Vahidian +2

Human-robot interaction (HRI) is a rapidly growing field that encompasses social and industrial applications. Machine learning plays a vital role in industrial HRI by enhancing the…

cs.CV2023★ 2 cited

Efficient Backdoor Removal Through Natural Gradient Fine-tuning

Nazmul Karim, Abdullah Al Arafat, Umar Khalid +2

The success of a deep neural network (DNN) heavily relies on the details of the training scheme; e.g., training data, architectures, hyper-parameters, etc. Recent backdoor attacks…

cs.CV2023

SAVE: Spectral-Shift-Aware Adaptation of Image Diffusion Models for Text-driven Video Editing

Nazmul Karim, Umar Khalid, Mohsen Joneidi +2

Text-to-Image (T2I) diffusion models have achieved remarkable success in synthesizing high-quality images conditioned on text prompts. Recent methods have tried to replicate the su…