Summary of Less Yet Robust: Crucial Region Selection For Scene Recognition, by Jianqi Zhang and Mengxuan Wang and Jingyao Wang and Lingyu Si and Changwen Zheng and Fanjiang Xu
Less yet robust: crucial region selection for scene recognition
by Jianqi Zhang, Mengxuan Wang, Jingyao Wang, Lingyu Si, Changwen Zheng, Fanjiang Xu
First submitted to arxiv on: 23 Sep 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 The paper proposes a novel approach to scene recognition in low-quality images, particularly for aerial and underwater scenes. By introducing an adaptive selection mechanism that identifies important and robust regions with high-level features, the model can learn via these regions to avoid interference from degradation. The proposed method implements a learnable mask in the neural network, which filters high-level features by assigning weights to different regions of the feature matrix. A regularization term is also introduced to enhance the significance of key high-level feature regions. The paper’s contributions include a plug-and-play architecture that can be easily extended to other methods and an Underwater Geological Scene Classification dataset for evaluating the model’s effectiveness. Experimental results demonstrate the superiority and robustness of the proposed method over state-of-the-art techniques on two datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make scene recognition better in low-quality images, especially underwater ones. It does this by finding important parts of the picture and focusing on those instead of trying to understand the whole thing. This makes the model work better even when the pictures are blurry or dark. The researchers also made a special kind of neural network that can filter out unimportant parts of the picture. They tested their method on some datasets and it did much better than other methods. |
Keywords
» Artificial intelligence » Classification » Mask » Neural network » Regularization
