论文标题
针对对象操纵的手部掌握回归的群体分布转移的新基准。元学习可以提高标准吗？
A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?
论文作者
论文摘要
了解手动视觉的手动姿势为混合现实，辅助生活或人类机器人互动中的新应用打开了大门。大多数方法在平衡数据集上进行了培训和评估。这在现实世界应用中的用途有限；这些方法如何在未知对象的野外执行？我们为对象组分布在手中和对象姿势回归中提出了一种新颖的基准。然后，我们检验了以下假设：元学习基线姿势回归神经网络可以适应这些变化，并更好地推广到未知对象。我们的结果表明，根据先验知识的数量，对基线的改进是可衡量的。对于联合手对象构成回归的任务，我们观察到对元学习者的优化干扰。为了解决此问题并进一步改进方法，我们提供了一项全面的分析，该分析应作为此基准的未来工作的基础。
Understanding hand-object pose with computer vision opens the door to new applications in mixed reality, assisted living or human-robot interaction. Most methods are trained and evaluated on balanced datasets. This is of limited use in real-world applications; how do these methods perform in the wild on unknown objects? We propose a novel benchmark for object group distribution shifts in hand and object pose regression. We then test the hypothesis that meta-learning a baseline pose regression neural network can adapt to these shifts and generalize better to unknown objects. Our results show measurable improvements over the baseline, depending on the amount of prior knowledge. For the task of joint hand-object pose regression, we observe optimization interference for the meta-learner. To address this issue and improve the method further, we provide a comprehensive analysis which should serve as a basis for future work on this benchmark.
