Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913719343120384 |
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| author | Yoon, Youngseok Hong, Sangwoo Joo, Hyungjun Qin, Yao Jeong, Haewon Lee, Jungwoo |
| author_facet | Yoon, Youngseok Hong, Sangwoo Joo, Hyungjun Qin, Yao Jeong, Haewon Lee, Jungwoo |
| contents | Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the problem by i) adjusting a loss function, ii) decoupling classifier learning, or iii) proposing a new multi-head architecture called experts. In this paper, we tackle the problem from a different perspective to augment a training dataset to enhance the sample diversity of minority classes. Specifically, our method, namely Confusion-Pairing Mixup (CP-Mix), estimates the confusion distribution of the model and handles the data deficiency problem by augmenting samples from confusion pairs in real-time. In this way, CP-Mix trains the model to mitigate its weakness and distinguish a pair of classes it frequently misclassifies. In addition, CP-Mix utilizes a novel mixup formulation to handle the bias in decision boundaries that originated from the imbalanced dataset. Extensive experiments demonstrate that CP-Mix outperforms existing methods for long-tailed image recognition and successfully relieves the confusion of the classifier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition Yoon, Youngseok Hong, Sangwoo Joo, Hyungjun Qin, Yao Jeong, Haewon Lee, Jungwoo Computer Vision and Pattern Recognition Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the problem by i) adjusting a loss function, ii) decoupling classifier learning, or iii) proposing a new multi-head architecture called experts. In this paper, we tackle the problem from a different perspective to augment a training dataset to enhance the sample diversity of minority classes. Specifically, our method, namely Confusion-Pairing Mixup (CP-Mix), estimates the confusion distribution of the model and handles the data deficiency problem by augmenting samples from confusion pairs in real-time. In this way, CP-Mix trains the model to mitigate its weakness and distinguish a pair of classes it frequently misclassifies. In addition, CP-Mix utilizes a novel mixup formulation to handle the bias in decision boundaries that originated from the imbalanced dataset. Extensive experiments demonstrate that CP-Mix outperforms existing methods for long-tailed image recognition and successfully relieves the confusion of the classifier. |
| title | Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.07621 |