ReferIt3D Neural Listeners for Fine Grained3D Object Identification in Real World Scenes ECCV 2020

ECCV 2020

ReferIt3D Neural Listeners for Fine Grained3D Object Identification in Real World Scenes ECCV 2020

Aug 24, 2020
|
39 views
Details
A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020). The multi-authored paper titled, “ReferIt3DNet: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes,” details research to design neural networks capable of comprehending spoken references to distinguish a specific 3D object (from multiple distractors of the same fine-grained object class) in a real-world setting. Read the full article https://cemse.kaust.edu.sa/news/kaust-stanford-neural-network-paper-accepted-presentation-eccv-2020 For more information on -Professor Mohamed Elhoseiny's Computer Vision, Content AI Research Group https://cemse.kaust.edu.sa/vision-cair Follow KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division also on https://cemse.kaust.edu.sa facebook: https://www.facebook.com/cemseKAUST/ twitter: @cemseKAUST

Comments
loading...