15 citations · 18 across the 5 of their papers we have counts for
8 papers
VLRC: Vision-Language Reprojection Consistency as a scalable signal for better feed-forward 3D pretraining
Marwane Hariat, David Filliat, Antoine Manzanera
Feed-forward 3D models are commonly trained using either expensive geometric supervision or self-supervised photometric objectives, both of which provide incomplete learning signal…
Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models
Marwane Hariat, Gianni Franchi, David Filliat +1
We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic representation. We model view-le…
SS3D: End2End Self-Supervised 3D from Web Videos
Marwane Hariat, Gianni Franchi, David Filliat +1
We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly predicts depth, ego-motion, and…
Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks
Marwane Hariat, Antoine Manzanera, David Filliat
Monocular depth estimation (MDE) with self-supervised training approaches struggles in low-texture areas, where photometric losses may lead to ambiguous depth predictions. To addre…
Foundation Models and Transformers for Anomaly Detection: A Survey
Mouïn Ben Ammar, Arturo Mendoza, Nacim Belkhir +2
In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We ex…
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity
Mouïn Ben Ammar, David Brellmann, Arturo Mendoza +2
Out-of-distribution (OOD) detection is essential for ensuring the reliability and safety of machine learning systems. In recent years, it has received increasing attention, particu…
NECO: NEural Collapse Based Out-of-distribution detection
Mouïn Ben Ammar, Nacim Belkhir, Sebastian Popescu +2
Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypoth…
InfraParis: A multi-modal and multi-task autonomous driving dataset
Gianni Franchi, Marwane Hariat, Xuanlong Yu +3
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Cons…