2 citations · 6 across the 25 of their papers we have counts for
26 papers
ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection
Rashid Riyadh, Abd Ullah Khan, Imad Gohar +1
Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles. We propose region-of-intere…
The Professor: Multi-Teacher Unsupervised Prompt Distillation for Vision-Language Models
Ahmad Algadhi, Ahmed Alzuhair, Omar Alkhulaif +1
Prompt distillation compresses large vision-language models (VLMs) such as CLIP into lightweight student models by matching teacher predictions on unlabeled domain images. PromptKD…
Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraints
Omar Alshahrani, Muzammil Behzad
AI systems are being deployed across medical imaging faster than their failure modes are understood. At this point in time, the failure of greatest clinical concern is hallucinatio…
Spatio-Semantic Expert Routing Architecture with Mixture-of-Experts for Referring Image Segmentation
Alaa Dalaq, Muzammil Behzad
Referring image segmentation aims to produce a pixel-level mask for the image region described by a natural-language expression. Although pretrained vision-language models have imp…
XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models
Thuraya Alzubaidi, Sana Ammar, Maryam Alsharqi +2
Medical image segmentation is a critical component of clinical workflows, enabling accurate diagnosis, treatment planning, and disease monitoring. However, despite the superior per…
SwinTF3D: A Lightweight Multimodal Fusion Approach for Text-Guided 3D Medical Image Segmentation
Hasan Faraz Khan, Noor Fatima, Muzammil Behzad
The recent integration of artificial intelligence into medical imaging has driven remarkable advances in automated organ segmentation. However, most existing 3D segmentation framew…
Attack-Aware Deepfake Detection under Counter-Forensic Manipulations
Noor Fatima, Hasan Faraz Khan, Muzammil Behzad
This work presents an attack-aware deepfake and image-forensics detector designed for robustness, well-calibrated probabilities, and transparent evidence under realistic deployment…
AquaDiff: Diffusion-Based Underwater Image Enhancement for Addressing Color Distortion
Afrah Shaahid, Muzammil Behzad
Underwater images are severely degraded by wavelength-dependent light absorption and scattering, resulting in color distortion, low contrast, and loss of fine details that hinder v…
From Graphs to Gates: DNS-HyXNet, A Lightweight and Deployable Sequential Model for Real-Time DNS Tunnel Detection
Faraz Ali, Muhammad Afaq, Mahmood Niazi +1
Domain Name System (DNS) tunneling remains a covert channel for data exfiltration and command-and-control communication. Although graph-based methods such as GraphTunnel achieve st…
PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation
Hania Ghouse, Maryam Alsharqi, Farhad R. Nezami +1
Cardiac image analysis remains fragmented across tasks: anatomical segmentation, disease classification, and grounded clinical report generation are typically handled by separate n…
Scaling Down to Scale Up: Towards Operationally-Efficient and Deployable Clinical Models via Cross-Modal Low-Rank Adaptation for Medical Vision-Language Models
Thuraya Alzubaidi, Farhad R. Nezami, Muzammil Behzad
Foundation models trained via vision-language pretraining have demonstrated strong zero-shot capabilities across diverse image domains, yet their application to volumetric medical…
Generative Adversarial Synthesis and Deep Feature Discrimination of Brain Tumor MRI Images
Md Sumon Ali, Muzammil Behzad
Compared to traditional methods, Deep Learning (DL) becomes a key technology for computer vision tasks. Synthetic data generation is an interesting use case for DL, especially in t…
Prompt-Guided Patch UNet-VAE with Adversarial Supervision for Adrenal Gland Segmentation in Computed Tomography Medical Images
Hania Ghouse, Muzammil Behzad
Segmentation of small and irregularly shaped abdominal organs, such as the adrenal glands in CT imaging, remains a persistent challenge due to severe class imbalance, poor spatial…
Facial Emotion Learning with Text-Guided Multiview Fusion via Vision-Language Model for 3D/4D Facial Expression Recognition
Muzammil Behzad
Facial expression recognition (FER) in 3D and 4D domains presents a significant challenge in affective computing due to the complexity of spatial and temporal facial dynamics. Its…
Self-Supervised Multi-View Representation Learning using Vision-Language Model for 3D/4D Facial Expression Recognition
Muzammil Behzad
Facial expression recognition (FER) is a fundamental task in affective computing with applications in human-computer interaction, mental health analysis, and behavioral understandi…
Beam-Guided Knowledge Replay for Knowledge-Rich Image Captioning using Vision-Language Model
Reem AlJunaid, Muzammil Behzad
Generating informative and knowledge-rich image captions remains a challenge for many existing captioning models, which often produce generic descriptions that lack specificity and…
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement
Afrah Shaahid, Muzammil Behzad
Underwater images are often affected by complex degradations such as light absorption, scattering, color casts, and artifacts, making enhancement critical for effective object dete…
Deformable Attentive Visual Enhancement for Referring Segmentation Using Vision-Language Model
Alaa Dalaq, Muzammil Behzad
Image segmentation is a fundamental task in computer vision, aimed at partitioning an image into semantically meaningful regions. Referring image segmentation extends this task by…
MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation
Hania Ghouse, Muzammil Behzad
Efficient and accurate multi-organ segmentation from abdominal CT volumes is a fundamental challenge in medical image analysis. Existing 3D segmentation approaches are computationa…
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition
Muzammil Behzad
In this paper, we introduce MultiviewVLM, a vision-language model designed for unsupervised contrastive multiview representation learning of facial emotions from 3D/4D data. Our ar…
Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model
Muzammil Behzad, Guoying Zhao
In this paper, we introduce AffectVLM, a vision-language model designed to integrate multiviews for a semantically rich and visually comprehensive understanding of facial emotions…
Towards Reading Beyond Faces for Sparsity-Aware 4D Affect Recognition
Muzammil Behzad, Nhat Vo, Xiaobai Li +1
In this paper, we present a sparsity-aware deep network for automatic 4D facial expression recognition (FER). Given 4D data, we first propose a novel augmentation method to combat…
Landmarks-assisted Collaborative Deep Framework for Automatic 4D Facial Expression Recognition
Muzammil Behzad, Nhat Vo, Xiaobai Li +1
We propose a novel landmarks-assisted collaborative end-to-end deep framework for automatic 4D FER. Using 4D face scan data, we calculate its various geometrical images, and afterw…
Automatic 4D Facial Expression Recognition via Collaborative Cross-domain Dynamic Image Network
Muzammil Behzad, Nhat Vo, Xiaobai Li +1
This paper proposes a novel 4D Facial Expression Recognition (FER) method using Collaborative Cross-domain Dynamic Image Network (CCDN). Given a 4D data of face scans, we first com…