The scarcity of data in various scenarios, such as medical, industry and autonomous driving, leads to model overfitting and dataset imbalance, thus hindering effective detection and segmentation performance. Existing studies employ the generative models to synthesize more training samples to mitigate data scarcity. However, these synthetic samples are repetitive or simplistic and fail to provide "crucial information" that targets the downstream model's weaknesses. Additionally, these methods typically require separate training for different objects, leading to computational inefficiencies. To address these issues, we propose Crucial-Diff, a domain-agnostic framework designed to synthesize crucial samples. Our method integrates two key modules. The Scene Agnostic Feature Extractor (SAFE) utilizes a unified feature extractor to capture target information. The Weakness Aware Sample Miner (WASM) generates hard-to-detect samples using feedback from the detection results of downstream model, which is then fused with the output of SAFE module. Together, our Crucial-Diff framework generates diverse, high-quality training data, achieving a pixel-level AP of 83.63% and an F1-MAX of 78.12% on MVTec. On polyp dataset, Crucial-Diff reaches an mIoU of 81.64% and an mDice of 87.69%. Code is publicly available at https://github.com/JJessicaYao/Crucial-diff.

翻译：在医疗、工业和自动驾驶等多种场景中，数据稀缺会导致模型过拟合和数据集不平衡，从而阻碍有效的检测与分割性能。现有研究采用生成模型合成更多训练样本来缓解数据稀缺问题。然而，这些合成样本往往重复或过于简单，未能提供针对下游模型弱点的“关键信息”。此外，这些方法通常需要为不同对象单独训练，导致计算效率低下。为解决这些问题，我们提出了Crucial-Diff，一个领域无关的关键样本合成框架。我们的方法整合了两个关键模块：场景无关特征提取器（SAFE）利用统一特征提取器捕获目标信息；弱点感知样本挖掘器（WASM）基于下游模型检测结果的反馈生成难以检测的样本，并将其与SAFE模块的输出融合。通过Crucial-Diff框架，我们生成了多样化、高质量的训练数据，在MVTec数据集上实现了83.63%的像素级平均精度（AP）和78.12%的F1-MAX；在息肉数据集上达到了81.64%的平均交并比（mIoU）和87.69%的平均Dice系数（mDice）。代码已公开于https://github.com/JJessicaYao/Crucial-diff。