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Found 7 papers, 5 papers with code
CIZSL++: Creativity Inspired Generative Zero-Shot Learning
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.
Learning Diverse Generations using Determinantal Point Processes
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.
Creativity Inspired Zero-Shot Learning
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.
Video Summarization via Actionness Ranking
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.
Multi-Stream Dynamic Video Summarization
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.
From Third Person to First Person: Dataset and Baselines for Synthesis and Retrieval
In this work, we introduce two datasets (synthetic and natural/real) containing simultaneously recorded egocentric and exocentric videos.
GDPP: Learning Diverse Generations Using Determinantal Point Process
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images.
