76 citations · 181 across the 10 of their papers we have counts for
10 papers
Surfer-H Meets Holo1: Cost-Efficient Web Agent Powered by Open Weights
Mathieu Andreux, Breno Baldas Skuk, Hamza Benchekroun +41
We present Surfer-H, a cost-efficient web agent that integrates Vision-Language Models (VLM) to perform user-defined tasks on the web. We pair it with Holo1, a new open-weight coll…
Unifying GANs and Score-Based Diffusion as Generative Particle Models
Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos +4
Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle…
OCTET: Object-aware Counterfactual Explanations
Mehdi Zemni, Mickaël Chen, Éloi Zablocki +3
Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern.…
STEEX: Steering Counterfactual Explanations with Semantics
Paul Jacob, Éloi Zablocki, Hédi Ben-Younes +3
As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution f…
Raising context awareness in motion forecasting
Hédi Ben-Younes, Éloi Zablocki, Mickaël Chen +2
Learning-based trajectory prediction models have encountered great success, with the promise of leveraging contextual information in addition to motion history. Yet, we find that s…
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed +3
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs). We reveal a fundamental flaw of previous analyses which, by incorrectly modeling GA…
Stochastic Latent Residual Video Prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickaël Chen +2
Designing video prediction models that account for the inherent uncertainty of the future is challenging. Most works in the literature are based on stochastic image-autoregressive…
Unsupervised Object Segmentation by Redrawing
Mickaël Chen, Thierry Artières, Ludovic Denoyer
Object segmentation is a crucial problem that is usually solved by using supervised learning approaches over very large datasets composed of both images and corresponding object ma…
Multi-View Data Generation Without View Supervision
Mickaël Chen, Ludovic Denoyer, Thierry Artières
The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial ne…
Multi-view Generative Adversarial Networks
Mickaël Chen, Ludovic Denoyer
Learning over multi-view data is a challenging problem with strong practical applications. Most related studies focus on the classification point of view and assume that all the vi…