不用训练就能筛选高质量合成数据，让语义分割更高效。
A Training-free Synthetic Data Selection Method for Semantic Segmentation
- 用CLIP检测合成图像质量，剔除低质图像样本
- 通过标注与CLIP响应对比，过滤错误标注样本
- 减少一半数据量仍提升性能，适合数据增强场景
用合成数据训练语义分割模型因易获取和数量大而受到关注。以往方法通常生成大量合成图像-标注对并全部用于训练，但不可避免包含低质样本，会损害训练效果。本文提出一种无需训练的合成数据筛选（SDS）策略，基于CLIP实现高质量样本选择：首先设计基于扰动的CLIP相似性（PCS）评估合成图像可靠性，剔除低质图像；接着提出类别平衡的标注相似性过滤器（ASF），通过对比合成标注与CLIP响应，移除标注错误的样本。实验表明，使用该方法可将数据量减半，同时训练出的分割器性能更高。代码已开源：https://github.com/tanghao2000/SDS。
原文摘要 · Abstract (English)
Training semantic segmenter with synthetic data has been attracting great attention due to its easy accessibility and huge quantities. Most previous methods focused on producing large-scale synthetic image-annotation samples and then training the segmenter with all of them. However, such a solution remains a main challenge in that the poor-quality samples are unavoidable, and using them to train the model will damage the training process. In this paper, we propose a training-free Synthetic Data Selection (SDS) strategy with CLIP to select high-quality samples for building a reliable synthetic dataset. Specifically, given massive synthetic image-annotation pairs, we first design a Perturbation-based CLIP Similarity (PCS) to measure the reliability of synthetic image, thus removing samples with low-quality images. Then we propose a class-balance Annotation Similarity Filter (ASF) by comparing the synthetic annotation with the response of CLIP to remove the samples related to low-quality annotations. The experimental results show that using our method significantly reduces the data size by half, while the trained segmenter achieves higher performance. The code is released at https://github.com/tanghao2000/SDS.
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