Summary of Beyond Generation: Unlocking Universal Editing Via Self-supervised Fine-tuning, by Harold Haodong Chen et al.
Beyond Generation: Unlocking Universal Editing via Self-Supervised Fine-Tuning
by Harold Haodong Chen, Harry Yang, Ser-Nam Lim
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach called UES (Unlocking Universal Editing via Self-Supervision) that transforms video generation models into unified generation-editing systems. The strategy, which is lightweight and self-supervised, enables structured learning of intrinsic spatiotemporal correspondences through dual-conditioning mechanisms that use original video-text pairs to provide visual and textual semantics. This approach has several key advantages, including universality, unification, and efficiency. To evaluate the effectiveness of UES, the authors introduce a comprehensive benchmark called OmniBench-99, which consists of 99 videos across various categories, edited in different ways. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to edit videos using artificial intelligence. Right now, editing videos is difficult because we need to teach machines how to do it by showing them lots of examples. The problem with this approach is that it only works for specific types of videos and can be very slow. The authors of this paper propose a new way of editing videos that doesn’t require so much training and can work on many different types of videos. They call their method UES, or Unlocking Universal Editing via Self-Supervision. This approach is more efficient and universal than current methods. |
Keywords
» Artificial intelligence » Self supervised » Semantics » Spatiotemporal
