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框架核心是两阶段“粗筛-精滤”数据清洗流程。首先，利用CLIP的多门控决策逻辑进行宏观粗筛，精准剔除插画、图表等非摄影类噪声。随后，利用DINOv2的细粒度特征，创新采用“相对边际分数”识别处于类别边界的混淆样本，并结合GMM模型为各类别动态确定清洗标准。整个流程内置最小样本保留机制，以保护长尾分布下的稀有类别。 在清洗后的高纯度数据上，我们采用非对称协同训练策略。通过Swin Transformer与ConvNeXt双网络，利用其结构差异形成互补的错误识别模式，高效实现相互监督与纠错。同时，我们创新地对模型达成共识的样本目标化应用Balanced Softmax Loss，在精准应对长尾分布的同时，避免了放大噪声的影响。
☆207Nov 6, 2025Updated 3 months ago
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