Summary of Generated Contents Enrichment, by Mahdi Naseri et al.
Generated Contents Enrichment
by Mahdi Naseri, Jiayan Qiu, Zhou Wang
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel AI generation task, Generated Contents Enrichment (GCE), aims to produce realistic and coherent content by explicitly enriching both textual and visual domains. Unlike traditional tasks that rely on limited semantic descriptions, GCE seeks to generate content that is visually realistic, structurally coherent, and semantically abundant. To tackle this challenge, a deep end-to-end adversarial method is proposed, which first models the input description as a scene graph and then predicts additional enriching objects and their relationships using Graph Convolutional Networks. The enriched description is finally passed to an image synthesis model to generate the corresponding visual content. Experimental results on the Visual Genome dataset demonstrate the effectiveness of this approach in producing promising and visually plausible results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to create new artificial intelligence (AI) that can make pictures and text more realistic and useful. They’re doing this by giving AI a task called “Generated Contents Enrichment” or GCE. The goal is to make the content look real, make sense, and have lots of important details. To do this, they developed a special method using something called Graph Convolutional Networks. This helps the AI understand how different things relate to each other in an image. They tested their idea on some pictures and text from the Visual Genome dataset, and it worked pretty well! |
Keywords
» Artificial intelligence » Image synthesis
