18 citations · 27 across the 11 of their papers we have counts for
13 papers
Your Self-Supervised Projection Head Captures Object Co-Occurrence Statistics
Arthur Aubret, Jochen Triesch, Céline Teulière
Self-Supervised Learning (SSL) has achieved impressive success in learning semantic visual representations, yet the underlying principles driving this success remain underexplored.…
Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks
Mehdi Gharbage, Céline Teulière, Pierre Bouges +1
Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness for industrial visual inspe…
Predictive Coding Light+: learning to predict visual sequences with spike timing-dependent plasticity and synaptic delays
Antony W. N'dri, Thomas Barbier, Céline Teulière +1
The ability to predict the future is of great value for biological and artificial cognitive systems alike. However, successfully predicting the future typically requires maintainin…
Seeing the Whole in the Parts in Self-Supervised Representation Learning
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent successes in self-supervised learning (SSL) model spatial co-occurrences of visual features either by masking portions of an image or by aggressively cropping it. Here, we p…
Predictive Coding with Spiking Neural Networks: a Survey
Antony W. N'dri, William Gebhardt, Céline Teulière +4
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework o…
Self-supervised visual learning from interactions with objects
Arthur Aubret, Céline Teulière, Jochen Triesch
Self-supervised learning (SSL) has revolutionized visual representation learning, but has not achieved the robustness of human vision. A reason for this could be that SSL does not…
Blur aware metric depth estimation with multi-focus plenoptic cameras
Mathieu Labussière, Céline Teulière, Omar Ait-Aider
While a traditional camera only captures one point of view of a scene, a plenoptic or light-field camera, is able to capture spatial and angular information in a single snapshot, e…
Time to augment self-supervised visual representation learning
Arthur Aubret, Markus Ernst, Céline Teulière +1
Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to majo…
Visual Radial Basis Q-Network
Julien Hautot, Céline Teuliere, Nourddine Azzaoui
While reinforcement learning (RL) from raw images has been largely investigated in the last decade, existing approaches still suffer from a number of constraints. The high input di…
Embodied vision for learning object representations
Arthur Aubret, Céline Teulière, Jochen Triesch
Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto clo…
Leveraging blur information for plenoptic camera calibration
Mathieu Labussière, Céline Teulière, Frédéric Bernardin +1
This paper presents a novel calibration algorithm for plenoptic cameras, especially the multi-focus configuration, where several types of micro-lenses are used, using raw images on…
Blur Aware Calibration of Multi-Focus Plenoptic Camera
Mathieu Labussière, Céline Teulière, Frédéric Bernardin +1
This paper presents a novel calibration algorithm for Multi-Focus Plenoptic Cameras (MFPCs) using raw images only. The design of such cameras is usually complex and relies on preci…
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image
Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa +2
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for…