Summary of Lightning talks: AI for visual content creation

This is an AI generated summary. There may be inaccuracies.
Summarize another video · Purchase Premium

00:00:00 - 00:35:00

This video discusses how AI can be used for visual content creation, specifically with regards to text and images. The proposed Woodland Architecture is discussed, as well as how it can be used to create a coherent film from a paragraph of text. Three applications are shown, each demonstrating a different use for AI. Finally, the speaker talks about their work on an upgraded content recommendation engine for PowerPoint designer.

  • 00:00:00 The speaker is discussing how AI can be used to help with visual content creation, specifically with text and images being highly correlated. They are also discussing how AI can be used to simulate how children learn, specifically with regards to language and images.
  • 00:05:00 The video discusses the proposed Woodland Architecture, which is a two-tower system for visual content creation. This architecture is based on weak semantic correlation, which is a correlation between two variables that is not strong or consistent. The proposed system has advantages of efficiency and efficiencies for example, when using it online. The video also demonstrates an example of how the Woodland Architecture can be used to create a coherent film from a paragraph of text.
  • 00:10:00 The three applications shown are multimodal inspired AI creation, lightning talks, and face recognition. Each application showcases a different use for AI. The multimodal inspired AI creation application uses images and lyrics to create a coherent sentence. The lightning talks application uses AI to create a conversation between two people. The face recognition application uses AI to identify a person's face.
  • 00:15:00 The speaker will talk about their work on an upgraded content recommendation engine for PowerPoint designer, which uses machine intelligence to empower customers. The first function is automatic short video generation, which uses retrieval to look into a person's video usage and figure out what kind of videos they would be interested in. The second function is an upgraded content recommendation engine for PowerPoint designer, which uses machine learning to find comparable slides and content from other presentations and recommend similar slides and content to a user.
  • 00:20:00 This YouTube video shows how AI can be used to create short videos of a visual nature from text. The system first extracts sentences from a text summary model, and then retrieves videos or images from a library to create short videos. The key results of this system are the retrieval model based on the text and image, which is achieved through a Branch architecture and training with large-scale data. There are some limitations to the system, including the copyright issue and the functional role of the generated videos.
  • 00:25:00 The text to image retrieval model found in Designer can actually match the needs of designers, as it includes facial assets like images, videos, GIFs, illustrations, and stickers. The user query is typically a text query, so in this scenario, we need to perform the text to visual recommendation throughout the Library. Our system uses a multimodality paradigm, matching the semantics from the text to image. The previous system depends on a text to text retrieval, and it matches the query to the text associated with the images. This text cannot be very comprehensive, so there will miss something in the images. Another point is that since we use large-scale data in training the model, it has the zero short capability in retrieval when we have new images in the Library. We only need to index the images directly using the image encoder. Finally, in our system for the image encoder, we use the latest string transformer for text encoder, the touring track model. This is a brief summary of the comparison between current system and the previous system. We can see differences in the modality, the background model, the Training Method, and the data skills. In particular, it is easy for the current system to include new images to a library. Usually, we only
  • 00:30:00 Newer Infinity is a machine learning-based image generation system that supports generating images with arbitrary resolution and size.
  • 00:35:00 Nua Infinity is a general visual synthesis framework that can generate images and videos with arbitrary size you want. It is the technology behind Dolly, which affects its own high resolution images and videos. In the future, newer Infinity will speed up its training and influence to go large with skill in the next version.

Copyright © 2024 Summarize, LLC. All rights reserved. · Terms of Service · Privacy Policy · As an Amazon Associate, earns from qualifying purchases.