Video advertising is one of the most effective forms of advertisements because videos are more attractive, more persuasive, and more informative than images or texts. Increasing amounts of video advertisements requires faster and more intelligent technologies for video generation. We have developed a multi-modal video editing approach that can automatically generate advertising video clips from any source videos. We conduct chronological boundary segmentation of the original video and construct a weighted directed graph to assemble different segments. Experiments on our video editing datasets validate success of the proposed method in producing compelling and consistent advertising videos.

Image2latex usually means converts mathematical formulas in images into latex markup. It is a very challenging job due to the complex two-dimensional structure, variant scales of input, and very long representation sequence. Many researchers use encoder-decoder based model to solve this task and achieved good results. However, these methods don't make full use of the structure and position information of the formula. To solve this problem, we propose a global context-based network with transformer that can (1) learn a more powerful and robust intermediate representation via aggregating global features and (2) encode position information explicitly and (3) learn latent dependencies between symbols by using self-attention mechanism. The experimental results on the dataset IM2LATEX-100K demonstrate the effectiveness of our method.