Summary of Maefuse: Transferring Omni Features with Pretrained Masked Autoencoders For Infrared and Visible Image Fusion Via Guided Training, by Jiayang Li et al.
MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training
by Jiayang Li, Junjun Jiang, Pengwei Liang, Jiayi Ma, Liqiang Nie
First submitted to arxiv on: 17 Apr 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 Medium Difficulty summary: This paper presents MaeFuse, an innovative autoencoder model designed for Infrared and Visible Image Fusion (IVIF). Unlike existing approaches that rely on downstream tasks to emphasize target objects and achieve high-quality results, MaeFuse leverages a Masked Autoencoders (MAE) pretrained encoder to extract low-level reconstruction and high-level vision features at a low computational cost. To address the domain gap between different modal features and the block effect caused by the MAE encoder, the authors propose a guided training strategy that adjusts the fusion layer to the feature space of the encoder. This approach enables comprehensive integration of feature vectors from both infrared and visible modalities, preserving rich details in each modality. MaeFuse showcases impressive performance on various public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper introduces a new way to combine infrared and visible images called MaeFuse. It’s different from other methods because it doesn’t rely on specific tasks to work well. Instead, it uses a special kind of artificial intelligence called Masked Autoencoders (MAE) to extract important features from the images. The authors also developed a new training method to make sure that the combined images look good and preserve their original details. They tested MaeFuse on different datasets and found that it works well across various applications. |
Keywords
» Artificial intelligence » Autoencoder » Encoder » Mae
