Meta-learning has shown its effectiveness in adapting to new tasks with transferring the prior experience learned from other related tasks [7,34,38]. At ahigh level, the meta-learning process involves two steps: meta-training and meta-testing.

The main result is informally presented here.

The main result is informally presented here.

The computer graphics pipeline has achieved impressive results in generating high-quality images, while offering users a great level of freedom and controllability over the generated images. This has many applications in creating and editing content for the creative industries, such as films, games, scientific visualisation, and more recently, in generating training data for computer vision tasks.

The left part is about some existing approaches, e.g., IRNet [

Projects Agenc Researchw Air Force Number F authorsand plied, ofthe reproduce herein.

For VaTeX captioning and retrieval, we use the latest v1.1 version3, which contains 25,991 videos for training and 6,000 videos for public testing. The statistics can be found in Table 1. Visual genome synsets are pairs, where the keys are noisy natural language phrases and the values are the mapped WordNet synsets [6]. Ifavisualtokenoccurs in multiple frames, we use the averaged frame indexas its temporal indicator. Specifically,for UniVL, we set the number of epoches to be50 and the linear warmup steps to be40.