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Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
Aoming Liang, Qi Liu, Lei Xu +4
Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantit…
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
Sertac Kilickaya, Mete Ahishali, Cansu Celebioglu +5
The frequent breakdowns and malfunctions of industrial equipment have driven increasing interest in utilizing cost-effective and easy-to-deploy sensors, such as microphones, for ef…
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Ilke Adalioglu, Serkan Kiranyaz, Mete Ahishali +5
Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocard…
Blind Underwater Image Restoration using Co-Operational Regressor Networks
Ozer Can Devecioglu, Serkan Kiranyaz, Turker Ince +1
The exploration of underwater environments is essential for applications such as biological research, archaeology, and infrastructure maintenanceHowever, underwater imaging is chal…
Relevance-guided Audio Visual Fusion for Video Saliency Prediction
Li Yu, Xuanzhe Sun, Pan Gao +1
Audio data, often synchronized with video frames, plays a crucial role in guiding the audience's visual attention. Incorporating audio information into video saliency prediction ta…
High-Frequency Enhanced Hybrid Neural Representation for Video Compression
Li Yu, Zhihui Li, Jimin Xiao +1
Neural Representations for Videos (NeRV) have simplified the video codec process and achieved swift decoding speeds by encoding video content into a neural network, presenting a pr…
High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs
Ozer Can Devecioglu, Serkan Kiranyaz, Zafer Yilmaz +3
Vibration sensors are essential in acquiring seismic activity for an accurate earthquake assessment. The state-of-the-art sensors can provide the best signal quality and the highes…
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism
Yu Xue, Pengcheng Jiang, Chenchen Zhu +3
Neural architecture search (NAS) has emerged as a powerful paradigm that enables researchers to automatically explore vast search spaces and discover efficient neural networks. How…
BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim +1
Objective: Many studies on radar signal restoration in the literature focus on isolated restoration problems, such as denoising over a certain type of noise, while ignoring other t…
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
Dynamic Facial Expression Recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologi…
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
Farhad Pakdaman, Moncef Gabbouj
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This app…
Revisiting Generative Adversarial Networks for Binary Semantic Segmentation on Imbalanced Datasets
Lei Xu, Moncef Gabbouj
Anomalous crack region detection is a typical binary semantic segmentation task, which aims to detect pixels representing cracks on pavement surface images automatically by algorit…
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs
Yuxin Xie, Li Yu, Farhad Pakdaman +1
Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as hi…
Panoramic Image Inpainting With Gated Convolution And Contextual Reconstruction Loss
Li Yu, Yanjun Gao, Farhad Pakdaman +1
Deep learning-based methods have demonstrated encouraging results in tackling the task of panoramic image inpainting. However, it is challenging for existing methods to distinguish…
Pixel-Wise Color Constancy via Smoothness Techniques in Multi-Illuminant Scenes
Umut Cem Entok, Firas Laakom, Farhad Pakdaman +1
Most scenes are illuminated by several light sources, where the traditional assumption of uniform illumination is invalid. This issue is ignored in most color constancy methods, pr…
Perceptual Learned Image Compression via End-to-End JND-Based Optimization
Farhad Pakdaman, Sanaz Nami, Moncef Gabbouj
Emerging Learned image Compression (LC) achieves significant improvements in coding efficiency by end-to-end training of neural networks for compression. An important benefit of th…
Trustworthiness of Users: A One-Class Classification Approach
Tanveer Khan, Fahad Sohrab, Antonis Michalas +1
(formerly Twitter) is a prominent online social media platform that plays an important role in sharing information making the content generated on this platform a valu…
Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification
Muhammad Uzair Zahid, Aysen Degerli, Fahad Sohrab +4
Early detection of myocardial infarction (MI), a critical condition arising from coronary artery disease (CAD), is vital to prevent further myocardial damage. This study introduces…
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Lei Xu, Mete Ahishali, Moncef Gabbouj
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies. Howe…
Class-wise Generalization Error: an Information-Theoretic Analysis
Firas Laakom, Yuheng Bu, Moncef Gabbouj
Existing generalization theories of supervised learning typically take a holistic approach and provide bounds for the expected generalization over the whole data distribution, whic…
Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features
Ali Falahati, Mohammad Karim Safavi, Ardavan Elahi +2
Providing high-quality video with efficient bitrate is a main challenge in video industry. The traditional one-size-fits-all scheme for bitrate ladders is inefficient and reaching…
Fusion of Quadratic Time-Frequency Analysis and Convolutional Neural Networks to Diagnose Bearing Faults Under Time-Varying Speeds
Mohammad Al-Sa'd, Tuomas Jalonen, Serkan Kiranyaz +1
Diagnosis of bearing faults is paramount to reducing maintenance costs and operational breakdowns. Bearing faults are primary contributors to machine vibrations, and analyzing thei…