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冷的CLIP-DINO:对于弱监督的语义细分来说,这是一个强大的支柱
概括
我们介绍了WeCLIP和WeCLIP+,这是使用CLIP和DINO模型进行弱监督语义细分的新单阶段管道. 这些方法可以在降低培训成本的情况下获得最先进的结果.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 弱监督的语义细分 (WSSS) 通常使用图像级标签.
- 现有的WSSS方法经常使用CLIP等模型来生成伪标签,用于单独的细分模型训练.
- 没有先前的工作直接使用CLIP作为直接对象细分的骨干,使用图像级标签.
研究的目的:
- 为WSSS提出WeCLIP和WeCLIP+,为WSSS提供单阶段管道.
- 利用冷的CLIP和DINO模型进行直接的语义特征提取和细分.
- 引入改进模块,以优化伪标签和提高细分精度.
主要方法:
- WeCLIP使用冷的CLIP模型作为特征提取的骨干,以及用于预测的轻量级解码器.
- WeCLIP+将冷的CLIP和DINO模型结合起来,作为增强特征提取的支柱.
- 这两种方法都使用了改进模块 (RFM/RFM+) 来在培训期间动态优化伪标签.
主要成果:
- WeCLIP和WeCLIP+显著优于现有的WSSS方法.
- WeCLIP+取得了较高的欧盟交叉点 (mIoU) 平均得分:83.9%的VOC2012和56.3%的COCO.
- 这两种模型在完全监督的环境中都表现出了竞争力.
结论:
- WeCLIP和WeCLIP+为WSSS提供高效和有效的单阶段解决方案.
- 整合CLIP和DINO模型增强了特征表示,改善了细分.
- 这些方法可以降低培训成本,同时实现最先进的性能.
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