Long-Tailed Visual Recognition via Permutation-Invariant Head-to-Tail Feature Fusion
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908388015734784 |
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| author | Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-ming Huang, Hui |
| author_facet | Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-ming Huang, Hui |
| contents | The imbalanced distribution of long-tailed data presents a significant challenge for deep learning models, causing them to prioritize head classes while neglecting tail classes. Two key factors contributing to low recognition accuracy are the deformed representation space and a biased classifier, stemming from insufficient semantic information in tail classes. To address these issues, we propose permutation-invariant and head-to-tail feature fusion (PI-H2T), a highly adaptable method. PI-H2T enhances the representation space through permutation-invariant representation fusion (PIF), yielding more clustered features and automatic class margins. Additionally, it adjusts the biased classifier by transferring semantic information from head to tail classes via head-to-tail fusion (H2TF), improving tail class diversity. Theoretical analysis and experiments show that PI-H2T optimizes both the representation space and decision boundaries. Its plug-and-play design ensures seamless integration into existing methods, providing a straightforward path to further performance improvements. Extensive experiments on long-tailed benchmarks confirm the effectiveness of PI-H2T. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-Tailed Visual Recognition via Permutation-Invariant Head-to-Tail Feature Fusion Li, Mengke Hu, Zhikai Lu, Yang Lan, Weichao Cheung, Yiu-ming Huang, Hui Computer Vision and Pattern Recognition The imbalanced distribution of long-tailed data presents a significant challenge for deep learning models, causing them to prioritize head classes while neglecting tail classes. Two key factors contributing to low recognition accuracy are the deformed representation space and a biased classifier, stemming from insufficient semantic information in tail classes. To address these issues, we propose permutation-invariant and head-to-tail feature fusion (PI-H2T), a highly adaptable method. PI-H2T enhances the representation space through permutation-invariant representation fusion (PIF), yielding more clustered features and automatic class margins. Additionally, it adjusts the biased classifier by transferring semantic information from head to tail classes via head-to-tail fusion (H2TF), improving tail class diversity. Theoretical analysis and experiments show that PI-H2T optimizes both the representation space and decision boundaries. Its plug-and-play design ensures seamless integration into existing methods, providing a straightforward path to further performance improvements. Extensive experiments on long-tailed benchmarks confirm the effectiveness of PI-H2T. |
| title | Long-Tailed Visual Recognition via Permutation-Invariant Head-to-Tail Feature Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00625 |