# A Training-free Synthetic Data Selection Method for Semantic Segmentation

arXiv: https://arxiv.org/abs/2501.15201 · HTML: https://mlsanity.com/paper/2501.15201/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [ScalSelect: Scalable Training-Free Multimodal Data Selection for Efficient Visual Instruction Tuning](https://mlsanity.com/paper/2602.11636/) (2026)
- [JoDiffusion: Jointly Diffusing Image with Pixel-Level Annotations for Semantic Segmentation Promotion](https://mlsanity.com/paper/2512.13014/) (2025)
- [NRSeg: Noise-Resilient Learning for BEV Semantic Segmentation via Driving World Models](https://mlsanity.com/paper/2507.04002/) (2025)
- [Task-Oriented Data Synthesis and Control-Rectify Sampling for Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2512.16740/) (2025)
- [OMUDA: Omni-level Masking for Unsupervised Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2512.12303/) (2025)
- [A Real-Calibrated Synthetic-First Data Engine](https://mlsanity.com/paper/2605.09699/) (2026)
- [UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation](https://mlsanity.com/paper/2605.14626/) (2026)
- [What Makes Synthetic Data Effective in Image Segmentation](https://mlsanity.com/paper/2605.19289/) (2026)
- [Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting](https://mlsanity.com/paper/2607.02637/) (2026)

## Abstract (arXiv)

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.