Abstract
Learning methods using synthetic data have attracted attention as an effective approach for increasing the diversity of training data while reducing collection costs, thereby improving the robustness of model discrimination. However, many existing methods improve robustness only indirectly through the diversification of training samples and do not explicitly teach the model which regions in the input space truly contribute to discrimination; consequently, the model may learn spurious correlations caused by synthesis biases and artifacts. Motivated by this limitation, this paper proposes a learning framework that uses provenance information obtained during the training data synthesis process, indicating whether each region in the input space originates from the target object, as an auxiliary supervisory signal to promote the acquisition of representations focused on target regions. Specifically, input gradients are decomposed based on information about target and non-target regions during synthesis, and input gradient guidance is introduced to suppress gradients over non-target regions. This suppresses the model's reliance on non-target regions and directly promotes the learning of discriminative representations for target regions. Experiments demonstrate the effectiveness and generality of the proposed method across multiple tasks and modalities, including weakly supervised object localization, spatio-temporal action localization, and image classification.
Abstract (translated)
基于合成数据的学习方法因其能有效提升训练数据多样性、降低采集成本，进而增强模型判别鲁棒性而受到关注。然而，现有方法大多仅通过训练样本多样化间接提升鲁棒性，并未明确指导模型识别输入空间中真正贡献判别能力的区域；因此模型可能习得由合成偏差与伪影导致的虚假关联。针对这一局限，本文提出一种利用训练数据合成过程中获取的溯源信息作为辅助监督信号的学习框架，该信息指示输入空间中各区域是否源自目标对象，从而促进模型聚焦于目标区域的表征学习。具体而言，在合成过程中基于目标与非目标区域的信息对输入梯度进行分解，并引入输入梯度引导机制以抑制非目标区域的梯度响应。该方法可抑制模型对非目标区域的依赖，并直接促进目标区域判别性表征的学习。实验表明，所提方法在弱监督目标定位、时空动作定位及图像分类等多个任务与模态中均具有有效性与通用性。
URL
https://arxiv.org/abs/2604.02946
