7 citations · 8 across the 7 of their papers we have counts for
11 papers
Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization
Guillaume Jeanneret, Mathis Koroglu, Hugo Caselles-Dupré +2
Diffusion Transformer Text-to-Video models have achieved remarkable synthesis quality, yet fine-grained spatial controllability remains a significant challenge. While existing trai…
FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion
Hugo Caselles-Dupré, Mathis Koroglu, Guillaume Jeanneret +2
Diffusion-based image-to-video (I2V) models are increasingly effective, yet they struggle to scale to ultra-high-resolution inputs (e.g., 4K). Generating videos at the model's nati…
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Visual transformers have achieved remarkable performance in image classification tasks, but this performance gain has come at the cost of interpretability. One of the main obstacle…
Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
This paper addresses the challenge of generating Counterfactual Explanations (CEs), involving the identification and modification of the fewest necessary features to alter a classi…
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
Angela Castillo, Maria Escobar, Guillaume Jeanneret +4
Mixed reality applications require tracking the user's full-body motion to enable an immersive experience. However, typical head-mounted devices can only track head and hand moveme…
Adversarial Counterfactual Visual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial att…
Diffusion Models for Counterfactual Explanations
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
Counterfactual explanations have shown promising results as a post-hoc framework to make image classifiers more explainable. In this paper, we propose DiME, a method allowing the g…
A Hierarchical Assessment of Adversarial Severity
Guillaume Jeanneret, Juan C Perez, Pablo Arbelaez
Adversarial Robustness is a growing field that evidences the brittleness of neural networks. Although the literature on adversarial robustness is vast, a dimension is missing in th…
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks. In this work, we study how equipping models with Test-time Transformatio…
Gabor Layers Enhance Network Robustness
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret +4
We revisit the benefits of merging classical vision concepts with deep learning models. In particular, we explore the effect on robustness against adversarial attacks of replacing…
MAIN: Multi-Attention Instance Network for Video Segmentation
Juan Leon Alcazar, Maria A. Bravo, Ali K. Thabet +4
Instance-level video segmentation requires a solid integration of spatial and temporal information. However, current methods rely mostly on domain-specific information (online lear…