Summary of Xt: Nested Tokenization For Larger Context in Large Images, by Ritwik Gupta et al.
xT: Nested Tokenization for Larger Context in Large Images
by Ritwik Gupta, Shufan Li, Tyler Zhu, Jitendra Malik, Trevor Darrell, Karttikeya Mangalam
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel computer vision framework called xT is introduced for effectively aggregating global context with local details in large images. The traditional approaches of down-sampling or cropping incur significant losses, making it necessary to choose which information to discard. xT addresses this issue by being a simple framework for vision transformers that can model large images end-to-end on contemporary GPUs. A set of benchmark datasets across classic vision tasks is used to assess the method’s improvement in understanding truly large images and incorporating fine details over large scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a huge satellite image that contains important information about the Earth’s surface. Currently, computer vision models can’t handle such large images well because they either lose too much detail or can’t see the big picture. To solve this problem, researchers have developed a new framework called xT. It allows them to process very large images and still capture both the small details and the overall context. This has improved their accuracy in certain tasks by up to 8.6% and their ability to segment objects from images that are as large as 29,000 x 29,000 pixels. |
