1 citations · 3 across the 8 of their papers we have counts for
8 papers
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…
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…
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…