CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916038389530624 |
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| author | Li, Mengke Ling, Haiquan Chen, Lihao Lu, Yang Zhang, Yiqun Huang, Hui |
| author_facet | Li, Mengke Ling, Haiquan Chen, Lihao Lu, Yang Zhang, Yiqun Huang, Hui |
| contents | Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these issues but typically ignore the non-uniform impact of label noise across classes, resulting in ineffective correction for tail classes and over-regularization for head classes. To address this issue, we propose Class-Adaptive Rectification with Experts (CARE), a parameter-efficient framework that leverages three complementary supervision sources from vision-language models (VLM): observed noisy labels, VLM text embeddings, and visual features. CARE introduces a class-adaptive expert consensus mechanism that enforces stricter agreement for tail classes and more permissive agreement for head classes based on class frequency. By aggregating high-confidence predictions across these sources, CARE filters unreliable signals and recalibrates class distributions, yielding more reliable rectification under long-tailed distributions. Extensive experiments on both synthetic and real-world benchmarks demonstrate that CARE consistently outperforms state-of-the-art methods, achieving up to 3.0\% performance gains. The source code is available at https://github.com/qwq123-study/CARE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23254 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels Li, Mengke Ling, Haiquan Chen, Lihao Lu, Yang Zhang, Yiqun Huang, Hui Computer Vision and Pattern Recognition Learning from real-world data is frequently hindered by the compound challenge of long-tailed class distributions and noisy annotations. Existing methods partially address these issues but typically ignore the non-uniform impact of label noise across classes, resulting in ineffective correction for tail classes and over-regularization for head classes. To address this issue, we propose Class-Adaptive Rectification with Experts (CARE), a parameter-efficient framework that leverages three complementary supervision sources from vision-language models (VLM): observed noisy labels, VLM text embeddings, and visual features. CARE introduces a class-adaptive expert consensus mechanism that enforces stricter agreement for tail classes and more permissive agreement for head classes based on class frequency. By aggregating high-confidence predictions across these sources, CARE filters unreliable signals and recalibrates class distributions, yielding more reliable rectification under long-tailed distributions. Extensive experiments on both synthetic and real-world benchmarks demonstrate that CARE consistently outperforms state-of-the-art methods, achieving up to 3.0\% performance gains. The source code is available at https://github.com/qwq123-study/CARE. |
| title | CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.23254 |