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.
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-Professor Mohamed Elhoseiny's Computer Vision, Content AI Research Group https://cemse.kaust.edu.sa/vision-cair
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