This paper addresses the problem of unsupervised video summarization, formulated as
selecting a sparse subset of video frames that optimally represent the input video. Our key …

Our goal is to predict future video frames given a sequence of input frames. Despite large
amounts of video data, this remains a challenging task because of the high-dimensionality of …

We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks
(GANs) and its conditional variants to address the well known problem of mode collapse …

Point processes are becoming very popular in modeling asynchronous sequential data due
to their sound mathematical foundation and strength in modeling a variety of real-world …

The growth of component libraries puts them on a collision course with a key reuse problem-
the difficulty in scaling reuse libraries in both component sizes and feature variations …

Video summarization targets the challenge of finding the smallest subset of frames, while
still conveying the whole story of a given video. Thus it is of great significance for large-scale …

Generating an image from its description is a challenging task worth solving because of its
numerous practical applications ranging from image editing to virtual reality. All existing …

Generative adversarial networks (GANs) have enjoyed tremendous success in image
generation and processing and have recently attracted growing interest in financial …

Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models,
which have attracted great attention in various applications. However, this framework has …

Diagrams often depict complex phenomena and serve as a good test bed for visual and
textual reasoning. However, understanding diagrams using natural image understanding …