67 citations · 182 across the 19 of their papers we have counts for
21 papers
Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations
AmirHossein Eshghi, Hamid Saadatfar, Seyyed Ali Hoseini +2
Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence. Its purpose is intuitive: it converts internal mod…
WildRelight: A Real-World Benchmark and Physics-Guided Adaptation for Single-Image Relighting
Lezhong Wang, Mehmet Onurcan Kaya, Siavash Bigdeli +1
Recent single-image relighting methods, powered by advanced generative models, have achieved impressive photorealism on synthetic benchmarks. However, their effectiveness in the co…
Improving Credit Card Fraud Detection with an Optimized Explainable Boosting Machine
Reza E. Fazel, Arash Bakhtiary, Siavash A. Bigdeli
Addressing class imbalance is a central challenge in credit card fraud detection, as it directly impacts predictive reliability in real-world financial systems. To overcome this, t…
Onto-Epistemological Analysis of AI Explanations
Martina Mattioli, Eike Petersen, Aasa Feragen +2
Artificial intelligence (AI) is being applied in almost every field. At the same time, the currently dominant deep learning methods are fundamentally black-box systems that lack ex…
ReLumix: Extending Image Relighting to Video via Video Diffusion Models
Lezhong Wang, Shutong Jin, Ruiqi Cui +3
Controlling illumination during video post-production is a crucial yet elusive goal in computational photography. Existing methods often lack flexibility, restricting users to cert…
Membership Inference Attacks for Face Images Against Fine-Tuned Latent Diffusion Models
Lauritz Christian Holme, Anton Mosquera Storgaard, Siavash Arjomand Bigdeli
The rise of generative image models leads to privacy concerns when it comes to the huge datasets used to train such models. This paper investigates the possibility of inferring if…
Materialist: Physically Based Editing Using Single-Image Inverse Rendering
Lezhong Wang, Duc Minh Tran, Ruiqi Cui +5
Achieving physically consistent image editing remains a significant challenge in computer vision. Existing image editing methods typically rely on neural networks, which struggle t…
MozzaVID: Mozzarella Volumetric Image Dataset
Pawel Tomasz Pieta, Peter Winkel Rasmussen, Anders Bjorholm Dahl +4
Influenced by the complexity of volumetric imaging, there is a shortage of established datasets useful for benchmarking volumetric deep-learning models. As a consequence, new and e…
Feature-Centered First Order Structure Tensor Scale-Space in 2D and 3D
Pawel Tomasz Pieta, Anders Bjorholm Dahl, Jeppe Revall Frisvad +2
The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user's choice of method parameters. W…
StereoDiffusion: Training-Free Stereo Image Generation Using Latent Diffusion Models
Lezhong Wang, Jeppe Revall Frisvad, Mark Bo Jensen +1
The demand for stereo images increases as manufacturers launch more XR devices. To meet this demand, we introduce StereoDiffusion, a method that, unlike traditional inpainting pipe…
Are Sex-based Physiological Differences the Cause of Gender Bias for Chest X-ray Diagnosis?
Nina Weng, Siavash Bigdeli, Eike Petersen +1
While many studies have assessed the fairness of AI algorithms in the medical field, the causes of differences in prediction performance are often unknown. This lack of knowledge a…
Optimizing the Consumption of Spiking Neural Networks with Activity Regularization
Simon Narduzzi, Siavash A. Bigdeli, Shih-Chii Liu +1
Reducing energy consumption is a critical point for neural network models running on edge devices. In this regard, reducing the number of multiply-accumulate (MAC) operations of De…
AIM 2020 Challenge on Image Extreme Inpainting
Evangelos Ntavelis, Andrés Romero, Siavash Bigdeli +1
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: c…
Efficient Blind-Spot Neural Network Architecture for Image Denoising
David Honzátko, Siavash A. Bigdeli, Engin Türetken +1
Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy ima…
GramGAN: Deep 3D Texture Synthesis From 2D Exemplars
Tiziano Portenier, Siavash Bigdeli, Orcun Goksel
We present a novel texture synthesis framework, enabling the generation of infinite, high-quality 3D textures given a 2D exemplar image. Inspired by recent advances in natural text…
Learning Generative Models using Denoising Density Estimators
Siavash A. Bigdeli, Geng Lin, Tiziano Portenier +2
Learning probabilistic models that can estimate the density of a given set of samples, and generate samples from that density, is one of the fundamental challenges in unsupervised…
Image Restoration using Plug-and-Play CNN MAP Denoisers
Siavash Bigdeli, David Honzátko, Sabine Süsstrunk +1
Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (M…
Detecting Memorization in ReLU Networks
Edo Collins, Siavash Arjomand Bigdeli, Sabine Süsstrunk
We propose a new notion of `non-linearity' of a network layer with respect to an input batch that is based on its proximity to a linear system, which is reflected in the non-negati…
FaceShop: Deep Sketch-based Face Image Editing
Tiziano Portenier, Qiyang Hu, Attila Szabó +3
We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features…
Deep Mean-Shift Priors for Image Restoration
Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro +1
In this paper we introduce a natural image prior that directly represents a Gaussian-smoothed version of the natural image distribution. We include our prior in a formulation of im…
Image Restoration using Autoencoding Priors
Siavash Arjomand Bigdeli, Matthias Zwicker
We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoe…