6 papers
CLIP-Joint-Detect: End-to-End Joint Training of Object Detectors with Contrastive Vision-Language Supervision
Behnam Raoufi, Hossein Sharify, Mohamad Mahdee Ramezanee +2
Conventional object detectors rely on cross-entropy classification, which can be vulnerable to class imbalance and label noise. We propose CLIP-Joint-Detect, a simple and detector-…
Ensemble-Guided Distillation for Compact and Robust Acoustic Scene Classification on Edge Devices
Hossein Sharify, Behnam Raoufi, Mahdy Ramezani +2
We present a compact, quantization-ready acoustic scene classification (ASC) framework that couples an efficient student network with a learned teacher ensemble and knowledge disti…
GradID: Adversarial Detection via Intrinsic Dimensionality of Gradients
Mohammad Mahdi Razmjoo, Mohammad Mahdi Sharifian, Saeed Bagheri Shouraki
Despite their remarkable performance, deep neural networks exhibit a critical vulnerability: small, often imperceptible, adversarial perturbations can lead to drastically altered m…
Human-Centric Anomaly Detection in Surveillance Videos Using YOLO-World and Spatio-Temporal Deep Learning
Mohammad Ali Etemadi Naeen, Hoda Mohammadzade, Saeed Bagheri Shouraki
Anomaly detection in surveillance videos remains a challenging task due to the diversity of abnormal events, class imbalance, and scene-dependent visual clutter. To address these i…
Parallel Data Processing in Quantum Machine Learning
Mehdi Ramezani, Sina Asadiyan Zargar, Abolfazl Bahrampour +2
We propose a Quantum Machine Learning (QML) framework that applies the core design principle of quantum algorithms-superposition, oracle, and interference-to accelerate training. B…
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability
Mohammad Hossein Najafi, Mohammad Morsali, Mohammadreza Pashanejad +3
Deep neural networks for medical image classification often fail to generalize consistently in clinical practice due to violations of the i.i.d. assumption and opaque decision-maki…