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20222024
most citedMulti-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification

11 citations · 16 across the 11 of their papers we have counts for

collaborators

11 papers

eess.IV202411 cited

Multi-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification

Salma Hassan, Hamad Al Hammadi, Ibrahim Mohammed +1

The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In…

cs.CV2024

Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models

Sanoojan Baliah, Qinliang Lin, Shengcai Liao +2

Despite promising progress in face swapping task, realistic swapped images remain elusive, often marred by artifacts, particularly in scenarios involving high pose variation, color…

cs.CV20241 cited

Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach

Muhammad Saad Saeed, Shah Nawaz, Muhammad Zaigham Zaheer +6

Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that…

cs.CV2024

Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning

Ans Munir, Faisal Z. Qureshi, Muhammad Haris Khan +1

Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs. Predicting compositions unseen during training is a challenging…

cs.CV2024

Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery

Siddharth Tourani, Ahmed Alwheibi, Arif Mahmood +1

Unsupervised landmarks discovery (ULD) for an object category is a challenging computer vision problem. In pursuit of developing a robust ULD framework, we explore the potential of…

cs.CV2023

Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning

Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1

Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background. Most current methods align domains by usi…