标题： 面向 unseen 视角的动作分割通用化研究 显示英文标题

标题： Towards Generalizing Temporal Action Segmentation to Unseen Views

摘要： 尽管在时间动作分割方面已经取得了显著进展，但将其推广到未见过的视角这一挑战仍未得到解决。 因此，我们定义了一种针对未见过视角的动作分割协议，在该协议下评估模型时的相机视角在训练过程中不可用。 这包括从顶部前视图转换到侧面视图，甚至更具有挑战性的是从外视角转换到内视角。 此外，我们提出了一种用于时间动作分割的方法来应对这一挑战。 我们的方法在序列和片段级别利用共享表示，以减少训练过程中视角差异的影响。 通过引入序列损失和动作损失，我们实现了这一点，这两者共同促进了不同视角下的视频和动作表示的一致性。 在Assembly101、IkeaASM和EgoExoLearn数据集上的评估表明了显著的改进，对于未见过的外视角，F1@50提高了12.8%，而对于未见过的内视角，改善幅度达到了54%。 显示英文摘要

摘要： While there has been substantial progress in temporal action segmentation, the challenge to generalize to unseen views remains unaddressed. Hence, we define a protocol for unseen view action segmentation where camera views for evaluating the model are unavailable during training. This includes changing from top-frontal views to a side view or even more challenging from exocentric to egocentric views. Furthermore, we present an approach for temporal action segmentation that tackles this challenge. Our approach leverages a shared representation at both the sequence and segment levels to reduce the impact of view differences during training. We achieve this by introducing a sequence loss and an action loss, which together facilitate consistent video and action representations across different views. The evaluation on the Assembly101, IkeaASM, and EgoExoLearn datasets demonstrate significant improvements, with a 12.8% increase in F1@50 for unseen exocentric views and a substantial 54% improvement for unseen egocentric views.