PEPR：基于特权事件的预测正则化用于领域泛化
PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
AI总结：
本文提出PEPR方法，通过利用特权信息提升领域泛化能力，改进RGB模型在昼夜域转移中的鲁棒性，优于基于对齐的基线方法。
摘要：
深度神经网络在视觉感知中对领域偏移高度敏感，这对现实部署构成重大挑战。为解决此问题，我们提出一种基于学习使用特权信息（LUPI）范式的跨模态框架，用于训练鲁棒的单模态RGB模型。我们利用事件相机作为特权信息的来源，仅在训练期间可用。两种模态具有互补特性：RGB流语义密集但领域依赖性强，而事件流稀疏但更具领域不变性。直接对齐两者特征效果不佳，因为这迫使RGB编码器模仿稀疏事件表示，从而丢失语义细节。为克服此问题，我们引入基于特权事件的预测正则化（PEPR），将LUPI重新框架化为共享潜在空间中的预测问题。而不是强制进行跨模态对齐，我们训练RGB编码器以预测基于事件的潜在特征，从而在不牺牲语义丰富性的情况下提升鲁棒性。所得到的独立RGB模型在昼夜域转移等场景中持续提升鲁棒性，优于基于对齐的基线方法在目标检测和语义分割任务中。
英文摘要：
Deep neural networks for visual perception are highly susceptible to domain shift, which poses a critical challenge for real-world deployment under conditions that differ from the training data. To address this domain generalization challenge, we propose a cross-modal framework under the learning using privileged information (LUPI) paradigm for training a robust, single-modality RGB model. We leverage event cameras as a source of privileged information, available only during training. The two modalities exhibit complementary characteristics: the RGB stream is semantically dense but domain-dependent, whereas the event stream is sparse yet more domain-invariant. Direct feature alignment between them is therefore suboptimal, as it forces the RGB encoder to mimic the sparse event representation, thereby losing semantic detail. To overcome this, we introduce Privileged Event-based Predictive Regularization (PEPR), which reframes LUPI as a predictive problem in a shared latent space. Instead of enforcing direct cross-modal alignment, we train the RGB encoder with PEPR to predict event-based latent features, distilling robustness without sacrificing semantic richness. The resulting standalone RGB model consistently improves robustness to day-to-night and other domain shifts, outperforming alignment-based baselines across object detection and semantic segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
