24 citations · 24 across the 5 of their papers we have counts for
6 papers · 1 filter
FreeOcc: Training-free Panoptic Occupancy Prediction via Foundation Models
Andrew Caunes, Thierry Chateau, Vincent Fremont
Semantic and panoptic occupancy prediction for road scene analysis provides a dense 3D representation of the ego vehicle's surroundings. Current camera-only approaches typically re…
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection
Ruddy Théodose, Dieumet Denis, Thierry Chateau +2
Current neural networks-based object detection approaches processing LiDAR point clouds are generally trained from one kind of LiDAR sensors. However, their performances decrease w…
Facial Landmark Correlation Analysis
Yongzhe Yan, Stefan Duffner, Priyanka Phutane +4
We present a facial landmark position correlation analysis as well as its applications. Although numerous facial landmark detection methods have been presented in the literature, f…
2D Wasserstein Loss for Robust Facial Landmark Detection
Yongzhe Yan, Stefan Duffner, Priyanka Phutane +4
The recent performance of facial landmark detection has been significantly improved by using deep Convolutional Neural Networks (CNNs), especially the Heatmap Regression Models (HR…
SMC Faster R-CNN: Toward a scene-specialized multi-object detector
Ala Mhalla, Thierry Chateau, Houda Maamatou +2
Generally, the performance of a generic detector decreases significantly when it is tested on a specific scene due to the large variation between the source training dataset and th…
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…