Summary of Symmetric Masking Strategy Enhances the Performance Of Masked Image Modeling, by Khanh-binh Nguyen and Chae Jung Park
Symmetric masking strategy enhances the performance of Masked Image Modeling
by Khanh-Binh Nguyen, Chae Jung Park
First submitted to arxiv on: 23 Aug 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 proposes a novel approach to masked image modeling (MIM) for self-supervised learning, which improves the preliminary training of vision transformers (ViTs). The proposed method, SymMIM, is designed to capture both global and local features, allowing it to outperform previous state-of-the-art (SOTA) methods across various tasks. By introducing a new masking strategy, SymMIM achieves an accuracy of 85.9% on ImageNet using ViT-Large and surpasses SOTA results in image classification, semantic segmentation, object detection, instance segmentation, and other downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from pictures without labels. It’s called masked image modeling (MIM), and it helps computers understand what they see. The old way of doing MIM was not very efficient, so the researchers came up with a new idea. They made a new program that can train models faster and better than before. This program is called SymMIM, and it does really well on lots of different tasks. |
Keywords
» Artificial intelligence » Image classification » Instance segmentation » Object detection » Self supervised » Semantic segmentation » Vit
