iNeMo：面向鲁棒类别增量学习的增量神经网格模型
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
AI总结：
针对现有持续学习方法难以应对分布外场景的问题，提出可随时间扩展的增量神经网格模型iNeMo，搭配隐空间初始化策略与位置正则化项，在分类任务的域内和OOD场景下均优于基线，还实现了首个姿态估计增量学习方法。
摘要：
与人类的学习特性不同，如今视觉任务中仍普遍采用仅在初始阶段基于固定数据集训练深度学习模型的做法。近期已有多种方法致力于处理持续数据流问题，但将这些方法拓展以应对分布外（OOD）场景的研究尚未有效开展。另一方面，近期研究表明，非持续式神经网格模型在泛化至此类OOD场景时表现出优异性能。\n为在持续学习场景中利用这一关键特性，我们提出增量神经网格模型，该模型可随时间推移新增网格进行扩展。此外，我们提出一种隐空间初始化策略，可提前为未来未见过的类别分配特征空间，还提出一种位置正则化项，强制不同类别的特征始终保持在各自的隐空间区域内。\n我们通过在Pascal3D和ObjectNet3D数据集上开展大量实验验证了方法的有效性，结果表明，我们的方法在分类任务上优于基线方法，域内场景下准确率高2-6%，OOD场景下高6-50%。我们的工作还提出了首个面向姿态估计的增量学习方法。代码和模型可在https://github.com/Fischer-Tom/iNeMo获取。
英文摘要：
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending these methods to manage out-of-distribution (OOD) scenarios has not effectively been investigated. On the other hand, it has recently been shown that non-continual neural mesh models exhibit strong performance in generalizing to such OOD scenarios. To leverage this decisive property in a continual learning setting, we propose incremental neural mesh models that can be extended with new meshes over time. In addition, we present a latent space initialization strategy that enables us to allocate feature space for future unseen classes in advance and a positional regularization term that forces the features of the different classes to consistently stay in respective latent space regions. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by $2-6\%$ in the in-domain and by $6-50\%$ in the OOD setting. Our work also presents the first incremental learning approach for pose estimation. Our code and model can be found at https://github.com/Fischer-Tom/iNeMo.
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。
