Summary of Sce-mae: Selective Correspondence Enhancement with Masked Autoencoder For Self-supervised Landmark Estimation, by Kejia Yin et al.
SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation
by Kejia Yin, Varshanth R. Rao, Ruowei Jiang, Xudong Liu, Parham Aarabi, David B. Lindell
First submitted to arxiv on: 28 May 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 This paper introduces a novel framework for self-supervised landmark estimation in facial images. The goal is to identify sparse facial landmarks without annotated data, which demands the formation of locally distinct feature representations. Existing methods rely on instance-level self-supervised learning and aggregate features into hypercolumns, but neglect the dense prediction nature of the task. SCE-MAE, a region-level SSL method, leverages vanilla feature maps and a Correspondence Approximation and Refinement Block (CARB) to directly hone local correspondences. The framework outperforms existing state-of-the-art methods by large margins on landmark matching and detection tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding special points on people’s faces without needing labeled data. It’s like solving a puzzle! Current best methods aren’t very good at this because they don’t take into account the details of the face. The new method, SCE-MAE, uses a different way to learn from pictures and focuses only on the important parts. This makes it much better than other methods at finding these special points. |
Keywords
» Artificial intelligence » Mae » Self supervised
