8 citations · 22 across the 17 of their papers we have counts for
14 papers · 1 filter
PSF-4D: A Progressive Sampling Framework for View Consistent 4D Editing
Hasan Iqbal, Nazmul Karim, Umar Khalid +4
Instruction-guided generative models, especially those using text-to-image (T2I) and text-to-video (T2V) diffusion frameworks, have advanced the field of content editing in recent…
EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing
Umar Khalid, Kashif Munir, Hasan Iqbal +6
Editing complex visual content from ambiguous or partially specified instructions remains a core challenge in vision-language modeling. Existing models can contextualize content bu…
The Role of Language Models in Modern Healthcare: A Comprehensive Review
Amna Khalid, Ayma Khalid, Umar Khalid
The application of large language models (LLMs) in healthcare has gained significant attention due to their ability to process complex medical data and provide insights for clinica…
Augmented Neural Fine-Tuning for Efficient Backdoor Purification
Nazmul Karim, Abdullah Al Arafat, Umar Khalid +2
Recent studies have revealed the vulnerability of deep neural networks (DNNs) to various backdoor attacks, where the behavior of DNNs can be compromised by utilizing certain types…
3DEgo: 3D Editing on the Go!
Umar Khalid, Hasan Iqbal, Azib Farooq +2
We introduce 3DEgo to address a novel problem of directly synthesizing photorealistic 3D scenes from monocular videos guided by textual prompts. Conventional methods construct a te…
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…
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…
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…
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…
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…
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning
Nazmul Karim, Umar Khalid, Ashkan Esmaeili +1
The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario…
Detect-and-describe: Joint learning framework for detection and description of objects
Addel Zafar, Umar Khalid
Traditional object detection answers two questions; "what" (what the object is?) and "where" (where the object is?). "what" part of the object detection can be fine-grained further…
RODD: A Self-Supervised Approach for Robust Out-of-Distribution Detection
Umar Khalid, Ashkan Esmaeili, Nazmul Karim +1
Recent studies have addressed the concern of detecting and rejecting the out-of-distribution (OOD) samples as a major challenge in the safe deployment of deep learning (DL) models.…
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning
Nazmul Karim, Umar Khalid, Nick Meeker +1
Though much work has been done in the domain of improving the adversarial robustness of facial recognition systems, a surprisingly small percentage of it has focused on self-superv…