Summary of Coarse-to-fine Tensor Trains For Compact Visual Representations, by Sebastian Loeschcke et al.
Coarse-To-Fine Tensor Trains for Compact Visual Representations
by Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen, Serge Belongie, Michael J. Kastoryano, Sagie Benaim
First submitted to arxiv on: 6 Jun 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 In this paper, researchers focus on developing compact and high-quality representations for visual data using tensor networks. The authors propose a novel method called ‘Prolongation Upsampling Tensor Train (PuTT)’ that learns tensor train representations in a coarse-to-fine manner. This approach is particularly useful for applications like novel view synthesis and 3D reconstruction, where compact and high-quality representations are essential. The paper evaluates the representation along three axes: compression, denoising capability, and image completion capability, using tasks such as image fitting, 3D fitting, and novel view synthesis. The results show improved performance compared to state-of-the-art tensor-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make computer representations of images more compact and useful. It uses special math called “tensor networks” to do this. The authors created a new method called PuTT that helps make these representations better. This is important for things like making new pictures from old ones or building 3D models. The paper tests this method using three ways: making the representation smaller, removing noise, and filling in missing parts. It does well compared to other similar methods. |
