Summary of Made to Order: Discovering Monotonic Temporal Changes Via Self-supervised Video Ordering, by Charig Yang et al.
Made to Order: Discovering monotonic temporal changes via self-supervised video ordering
by Charig Yang, Weidi Xie, Andrew Zisserman
First submitted to arxiv on: 25 Apr 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 This research paper introduces a transformer-based model for ordering image sequences of arbitrary length with built-in attribution maps. The model is trained to discover and localize monotonic changes in images, while ignoring cyclic and stochastic ones. By exploiting the temporal sequence as a supervisory signal, the model successfully detects object-level and environmental changes in unseen sequences across multiple domains. The attention-based attribution maps are used to segment the changing regions, and the learned representations can be applied to downstream tasks. This paper achieves state-of-the-art results on standard benchmarks for image ordering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a series of photos and trying to put them in order from earliest to latest. That’s basically what this research does, but instead of using human judgment, it uses a special computer model. This model looks at the sequence of images and figures out which ones have changed over time, while ignoring any changes that happen repeatedly or randomly. The model is really good at finding changes in different types of scenes and objects, and can even help us understand what’s changing by highlighting the important parts. Overall, this research has some exciting applications for analyzing and understanding visual data. |
Keywords
» Artificial intelligence » Attention » Transformer
