8 citations · 11 across the 4 of their papers we have counts for
5 papers
Image Segmentation-based Unsupervised Multiple Objects Discovery
Sandra Kara, Hejer Ammar, Florian Chabot +1
Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem…
Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks
Jaonary Rabarisoa, Valentin Belissen, Florian Chabot +1
We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks. It is based on a contrastive loss across views that compares pixel-level representa…
PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation
Abdallah Benzine, Florian Chabot, Bertrand Luvison +2
Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D p…
LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection
Florian Chabot, Quoc-Cuong Pham, Mohamed Chaouch
Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones. The trade-off between model performance and computational speed is…
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…