150 citations
- Université de BourgogneFR9 papers
- Centre National de la Recherche ScientifiqueFR4 papers
- Vision pour la RobotiqueFR4 papers
- Laboratoire Interdisciplinaire Carnot de BourgogneFR3 papers
- Institut CurieFR2 papers
- Maison des Sciences sociales et des Humanités de DijonFR2 papers
- Alstom (France)FR1 paper
- Centre de Recherche en Acquisition et Traitement de l'Image pour la SantéFR1 paper
- Centre Georges François LeclercFR1 paper
- CHU Dijon BourgogneFR1 paper
- Consejo Nacional de Investigaciones Científicas y TécnicasAR1 paper
- GREYCFR1 paper
11 papers
Leveraging Foundation Models To learn the shape of semi-fluid deformable objects
Omar El Assal, Carlos M. Mateo, Sebastien Ciron +1
One of the difficulties imposed on the manipulation of deformable objects is their characterization and the detection of representative keypoints for the purpose of manipulation. A…
Steering Prediction via a Multi-Sensor System for Autonomous Racing
Zhuyun Zhou, Zongwei Wu, Florian Bolli +5
Autonomous racing has rapidly gained research attention. Traditionally, racing cars rely on 2D LiDAR as their primary visual system. In this work, we explore the integration of an…
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau +2
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods e…
Learning Geodesic-Aware Local Features from RGB-D Images
Guilherme Potje, Renato Martins, Felipe Cadar +1
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfa…
Modality-Guided Subnetwork for Salient Object Detection
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the…
Learning With Context Feedback Loop for Robust Medical Image Segmentation
Kibrom Berihu Girum, Gilles Créhange, Alain Lalande
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixe…