Summary of A Refreshed Similarity-based Upsampler For Direct High-ratio Feature Upsampling, by Minghao Zhou et al.
A Refreshed Similarity-based Upsampler for Direct High-Ratio Feature Upsampling
by Minghao Zhou, Hong Wang, Yefeng Zheng, Deyu Meng
First submitted to arxiv on: 2 Jul 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 recently proposed similarity-based feature upsampling pipeline has achieved promising results for dense prediction tasks. However, it has specific limitations that restrict its applicability to a broader range of network structures. To address these issues, the authors propose an optimized framework called ReSFU, which features an explicitly controllable query-key feature alignment, a parameterized paired central difference convolution block for calculating similarity, and a fine-grained neighbor selection strategy on high-resolution features. The authors demonstrate that ReSFU is finely applicable to various types of architectures in a direct high-ratio upsampling manner and achieves satisfactory performance on different dense prediction applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For dense prediction tasks, feature upsampling is crucial. A popular pipeline uses high-resolution features as guidance to upsample low-resolution deep features based on local similarity. However, this approach has limitations that make it primarily applicable to hierarchical networks with iterative features as guidance. To overcome these issues, the authors develop a refreshed framework called ReSFU, which optimizes query-key feature alignment, calculates similarity using a parameterized paired central difference convolution block, and selects neighbors on high-resolution features. The result is a more generalizable and deployable method for upsampling features. |
Keywords
» Artificial intelligence » Alignment
