2 citations · 6 across the 9 of their papers we have counts for
11 papers
DERA: Detached Edge-Residual Adaptation for Prohibited item Detection
Yonathan Michael, Mohamad Alansari, Mohammed Bennamoun +3
Prohibited-item detection in X-ray imagery remains challenging due to object superposition, weak texture, and material clutter which obscure both semantic appearance and object bou…
Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
Ning Wang, Long Yu, Cong Hua +5
Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes. Current approaches typically correlate global visu…
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi +6
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a sing…
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
Divya Velayudhan, Abdelfatah Ahmed, Mohamad Alansari +10
Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However, current datasets are limited i…
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis
Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed +3
The preservation of aquatic biodiversity is critical in mitigating the effects of climate change. Aquatic scene understanding plays a pivotal role in aiding marine scientists in th…
BENet: A Cross-domain Robust Network for Detecting Face Forgeries via Bias Expansion and Latent-space Attention
Weihua Liu, Jianhua Qiu, Said Boumaraf +5
In response to the growing threat of deepfake technology, we introduce BENet, a Cross-Domain Robust Bias Expansion Network. BENet enhances the detection of fake faces by addressing…
Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review
Ahmad Obeid, Said Boumaraf, Anabia Sohail +5
Recent years witnessed remarkable progress in computational histopathology, largely fueled by deep learning. This brought the clinical adoption of deep learning-based tools within…
Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels
Maria Marrium, Arif Mahmood, Mohammed Bennamoun
Automatic annotation of large-scale datasets can introduce noisy training data labels, which adversely affect the learning process of deep neural networks (DNNs). Consequently, Noi…
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition
Qi Wang, Zhou Xu, Yuming Lin +6
Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by capturing pixel intensity changes with exceptional dynamic range, minimal latency, and en…
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi +3
This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology…
Language Model Guided Interpretable Video Action Reasoning
Ning Wang, Guangming Zhu, HS Li +3
While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches…