Summary of Sketchinr: a First Look Into Sketches As Implicit Neural Representations, by Hmrishav Bandyopadhyay et al.
SketchINR: A First Look into Sketches as Implicit Neural Representations
by Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song
First submitted to arxiv on: 14 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 The proposed SketchINR model advances the representation of vector sketches using implicit neural models. It compresses variable-length vector sketches into a fixed-dimensional latent space that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts xy point coordinates in a sketch at each time and stroke. SketchINR outperforms existing representations across multiple tasks, including data compression, representation fidelity, parallelization, and reproducing complex sketches with varying abstraction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SketchINR is a new way to represent vector sketches using a special kind of computer model called an implicit neural model. It takes in a sketch and shrinks it down into a smaller set of numbers that capture the important information about what the sketch looks like. This allows for big improvements in storing and processing sketches, making it easier to work with complex images. |
Keywords
» Artificial intelligence » Latent space
