Data augmentation using generative models has emerged as a powerful paradigm for enhancing performance in computer vision tasks. However, most existing augmentation approaches primarily focus on optimizing intrinsic data attributes -- such as fidelity and diversity -- to generate visually high-quality synthetic data, while often neglecting task-specific requirements. Yet, it is essential for data generators to account for the needs of downstream tasks, as training data requirements can vary significantly across different tasks and network architectures. To address these limitations, we propose UtilGen, a novel utility-centric data augmentation framework that adaptively optimizes the data generation process to produce task-specific, high-utility training data via downstream task feedback. Specifically, we first introduce a weight allocation network to evaluate the task-specific utility of each synthetic sample. Guided by these evaluations, UtilGen iteratively refines the data generation process using a dual-level optimization strategy to maximize the synthetic data utility: (1) model-level optimization tailors the generative model to the downstream task, and (2) instance-level optimization adjusts generation policies -- such as prompt embeddings and initial noise -- at each generation round. Extensive experiments on eight benchmark datasets of varying complexity and granularity demonstrate that UtilGen consistently achieves superior performance, with an average accuracy improvement of 3.87% over previous SOTA. Further analysis of data influence and distribution reveals that UtilGen produces more impactful and task-relevant synthetic data, validating the effectiveness of the paradigm shift from visual characteristics-centric to task utility-centric data augmentation.

翻译：基于生成模型的数据增强已成为提升计算机视觉任务性能的重要范式。然而，现有增强方法主要聚焦于优化数据的内在属性——如保真度与多样性——以生成视觉质量高的合成数据，却往往忽视任务特定的需求。由于不同任务及网络架构对训练数据的要求存在显著差异，数据生成器必须考虑下游任务的需求。为克服这些局限，本文提出UtilGen，一种以效用为中心的新型数据增强框架，通过下游任务反馈自适应优化数据生成过程，以产生任务专用、高实用性的训练数据。具体而言，我们首先引入权重分配网络评估每个合成样本的任务特定效用。在此评估引导下，UtilGen采用双层优化策略迭代优化数据生成过程以最大化合成数据效用：（1）模型级优化使生成模型适配下游任务；（2）实例级优化在每轮生成中调整生成策略——如提示嵌入与初始噪声。在八个不同复杂度与粒度的基准数据集上的大量实验表明，UtilGen始终取得更优性能，平均准确率较先前最优方法提升3.87%。对数据影响与分布的进一步分析显示，UtilGen生成的合成数据更具影响力且与任务更相关，验证了从以视觉特征为中心转向以任务效用为中心的数据增强范式的有效性。