Summary of Orformer: Occlusion-robust Transformer For Accurate Facial Landmark Detection, by Jui-che Chiang et al.
ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection
by Jui-Che Chiang, Hou-Ning Hu, Bo-Syuan Hou, Chia-Yu Tseng, Yu-Lun Liu, Min-Hung Chen, Yen-Yu Lin
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed ORFormer method tackles the issue of facial landmark detection (FLD) performance drops when faces are partially non-visible due to occlusions or extreme lighting conditions. The transformer-based approach associates each image patch with a messenger token, enabling consensus assessment between patches for identifying non-visible regions. By recovering missing features and leveraging them for FLD task heatmaps, ORFormer generates high-quality outputs resilient to partial occlusions. This method outperforms state-of-the-art approaches on challenging datasets like WFLW and COFW when integrated into existing FLD methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ORFormer is a new way to detect facial landmarks even when some parts of the face are hidden. Right now, computers struggle with this problem because they can’t accurately figure out what’s missing from the non-visible areas. The ORFormer method uses special “messenger” tokens that help it understand what’s going on in these hidden areas. By combining this new information, ORFormer can create really accurate maps of facial landmarks, even when some parts are missing. This means it can be used to improve existing face detection systems and make them work better with challenging images. |
Keywords
» Artificial intelligence » Token » Transformer
