no code implementations • 6 Jan 2025 • Arthur Aubret, Céline Teulière, Jochen Triesch
Here, we propose a new way to model spatial co-occurrences by aligning local representations (before pooling) with a global image representation.
no code implementations • 9 Sep 2024 • Antony W. N'dri, William Gebhardt, Céline Teulière, Fleur Zeldenrust, Rajesh P. N. Rao, Jochen Triesch, Alexander Ororbia
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding.
1 code implementation • 9 Jul 2024 • Arthur Aubret, Céline Teulière, Jochen Triesch
In our analysis, we find that the observed improvement is associated with a better viewpoint-wise alignment of different objects from the same category.
3 code implementations • 8 Aug 2023 • Mathieu Labussière, Céline Teulière, Omar Ait-Aider
A method to calibrate the inverse model is then proposed.
no code implementations • 27 Jul 2022 • Arthur Aubret, Markus Ernst, Céline Teulière, Jochen Triesch
Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation.
no code implementations • 12 May 2022 • Arthur Aubret, Céline Teulière, Jochen Triesch
During each play session the agent views an object in multiple orientations before turning its body to view another object.
3 code implementations • 9 Nov 2021 • Mathieu Labussière, Céline Teulière, Frédéric Bernardin, Omar Ait-Aider
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 only.
1 code implementation • 16 Apr 2020 • Mathieu Labussière, Céline Teulière, Frédéric Bernardin, Omar Ait-Aider
This paper presents a novel calibration algorithm for Multi-Focus Plenoptic Cameras (MFPCs) using raw images only.
no code implementations • CVPR 2017 • Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Céline Teulière, Thierry Chateau
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image.
Ranked #2 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)