Summary of Smcl: Saliency Masked Contrastive Learning For Long-tailed Recognition, by Sanglee Park et al.
SMCL: Saliency Masked Contrastive Learning for Long-tailed Recognition
by Sanglee Park, Seung-won Hwang, Jungmin So
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel approach called saliency masked contrastive learning to mitigate the issue of biased predictions when training models from imbalanced data. The problem arises when there are many more samples in one class than others, causing background features common to all classes to be unobserved in classes with fewer samples. This results in biased predictions that favor the majority class. The proposed method uses saliency detection to mask important parts of an image and then employs contrastive learning to move the masked image towards minority classes in the feature space. This helps eliminate the correlation between background features and original class, leading to improved generalizability. Experimental results demonstrate state-of-the-art performance on benchmark long-tailed datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in machine learning called biased predictions when training models from imbalanced data. Imbalanced data means that one group has way more examples than others, which can cause the model to make unfair predictions. The new method uses two techniques: saliency detection and contrastive learning. Saliency detection helps identify important parts of an image, while contrastive learning moves those parts towards minority groups in a special space called the feature space. This makes the model less biased and more fair. The results show that this new approach works really well on certain datasets. |
Keywords
» Artificial intelligence » Machine learning » Mask
