2 code implementations • 1 Jan 2021 • Mohamed Elhoseiny, Kai Yi, Mohamed Elfeki
To improve the discriminative power of ZSL, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art.
no code implementations • ICLR 2019 • Mohamed Elfeki, Camille Couprie, Mohamed Elhoseiny
Embedded in an adversarial training and variational autoencoder, our Generative DPP approach shows a consistent resistance to mode-collapse on a wide-variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, convergence-time, and generation quality.
2 code implementations • ICCV 2019 • Mohamed Elhoseiny, Mohamed Elfeki
We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen.
no code implementations • 1 Mar 2019 • Mohamed Elfeki, Ali Borji
Prior work proposed supervised and unsupervised algorithms to train models for learning the underlying behavior of humans by increasing modeling complexity or craft-designing better heuristics to simulate human summary generation process.
1 code implementation • 1 Dec 2018 • Mohamed Elfeki, Liqiang Wang, Ali Borji
With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content.
1 code implementation • 1 Dec 2018 • Mohamed Elfeki, Krishna Regmi, Shervin Ardeshir, Ali Borji
In this work, we introduce two datasets (synthetic and natural/real) containing simultaneously recorded egocentric and exocentric videos.
4 code implementations • 30 Nov 2018 • Mohamed Elfeki, Camille Couprie, Morgane Riviere, Mohamed Elhoseiny
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images.