8 citations · 10 across the 25 of their papers we have counts for
26 papers
AVAtt: A Free-Viewing Eye-Tracking Dataset and Benchmark for Visual Attention on Paintings
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
Saliency and scanpath models are developed almost exclusively on natural photographs, yet paintings are composed by artists to guide the viewer's eye and differ from photographs in…
The Ethics of Artificial Intelligence in Military Operations
Nicolas Drapier, Florian Mauberger, Aladine Chetouani +1
Deep learning systems now mediate military decisions to use force, yet their internal logic resists inspection, their evaluation practices are gameable, and their deployment fractu…
GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation
Mohammed Oussama Benyahia, Marouane Tliba, Mohamed Amine Kerkouri +10
Medical image segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific training. We introduce GazeRefine…
CGS-SLAM: Collaborative Gaussian Splatting based SLAM for Multi-Agent Reconstruction
Jean-Daniel de Ambrogi, Aladine Chetouani, Vincent Nguyen +1
Recent advances in SLAM have leveraged 3DGS for photorealistic reconstruction and novel view synthesis. However, most methods rely on RGB-D input, which is unavailable on consumer-…
Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +2
Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language models (MLLMs), given the sam…
Divergent Gaze Patterns in Artistic Viewing: Spatial and Temporal Signatures of Attention Across Autistic Individuals, Artists, and Neurotypical Observers
Mohammed Amine Kerkouri, Daphné Senggaran, Renaud Jusiak +7
How different populations visually explore artworks bears on cognitive science and on accessibility design, yet most eye-tracking work in autism has used social scenes rather than…
CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation
Yousra Nabila Taifour, Marouane Tliba, Zuheng Ming +9
Computed tomography (CT) images are frequently degraded by acquisition artifacts, including noise, blur, streaking, aliasing, and metal artifacts. Yet CT enhancement is still large…
GazeVaLM: A Multi-Observer Eye-Tracking Benchmark for Evaluating Clinical Realism in AI-Generated X-Rays
David Wong, Zeynep Isik, Bin Wang +22
We introduce GazeVaLM, a public eye-tracking dataset for studying clinical perception during chest radiograph authenticity assessment. The dataset comprises 960 gaze recordings fro…
What They Saw, Not Just Where They Looked: Semantic Scanpath Similarity via VLMs and NLP metric
Mohamed Amine Kerkouri, Marouane Tliba, Bin Wang +3
Scanpath similarity metrics are central to eye-movement research, yet existing methods predominantly evaluate spatial and temporal alignment while neglecting semantic equivalence b…
SPGen: Stochastic scanpath generation for paintings using unsupervised domain adaptation
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
Understanding human visual attention is key to preserving cultural heritage We introduce SPGen a novel deep learning model to predict scanpaths the sequence of eye movementswhen vi…
Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models
Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser +4
Knee osteoarthritis (KOA) diagnosis from radiographs remains challenging due to the subtle morphological details that standard deep learning models struggle to capture effectively.…
Combining Transformers and CNNs for Efficient Object Detection in High-Resolution Satellite Imagery
Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner
We present GLOD, a transformer-first architecture for object detection in high-resolution satellite imagery. GLOD replaces CNN backbones with a Swin Transformer for end-to-end feat…
Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading
Zhe Wang, Yuhua Ru, Aladine Chetouani +9
Automated grading of Knee Osteoarthritis (KOA) from radiographs is challenged by significant inter-observer variability and the limited robustness of deep learning models, particul…
Modeling Beyond MOS: Quality Assessment Models Must Integrate Context, Reasoning, and Multimodality
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +2
This position paper argues that Mean Opinion Score (MOS), while historically foundational, is no longer sufficient as the sole supervisory signal for multimedia quality assessment…
Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images
David C Wong, Bin Wang, Gorkem Durak +21
Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first ana…
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution
Zhe Wang, Yuhua Ru, Aladine Chetouani +7
Magnetic Resonance Imaging (MRI) at lower field strengths (e.g., 3T) suffers from limited spatial resolution, making it challenging to capture fine anatomical details essential for…
Eyes Tell the Truth: GazeVal Highlights Shortcomings of Generative AI in Medical Imaging
David Wong, Bin Wang, Gorkem Durak +23
The demand for high-quality synthetic data for model training and augmentation has never been greater in medical imaging. However, current evaluations predominantly rely on computa…
Feasibility study for reconstruction of knee MRI from one corresponding X-ray via CNN
Zhe Wang, Aladine Chetouani, Rachid Jennane
Generally, X-ray, as an inexpensive and popular medical imaging technique, is widely chosen by medical practitioners. With the development of medical technology, Magnetic Resonance…
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great Again
Zhe Wang, Yuhua Ru, Fabian Bauer +7
Magnetic Resonance Imaging (MRI) offers critical insights into microstructural details, however, the spatial resolution of standard 1.5T imaging systems is often limited. In contra…
Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI from X-ray: Integrating Radiographic Feature Information
Zhe Wang, Yung Hsin Chen, Aladine Chetouani +6
Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, often diagnosed using X-rays due to its cost-effectiveness. While Magnetic Resonance Imaging (MRI) provides super…
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis
Zhe Wang, Aladine Chetouani, Rachid Jennane +3
Knee Osteoarthritis (KOA) is a common musculoskeletal disorder that significantly affects the mobility of older adults. In the medical domain, images containing temporal data are f…
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)
Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner
The prediction of ship trajectories is a growing field of study in artificial intelligence. Traditional methods rely on the use of LSTM, GRU networks, and even Transformer architec…
Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment
Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri +4
Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to…
Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?
Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani +1
Neural network quantization is a critical technique for deploying models on resource-limited devices. Despite its widespread use, the impact of quantization on model perceptual fie…