论文标题
物体永久性沿着记忆随机步行出现
Object Permanence Emerges in a Random Walk along Memory
论文作者
论文摘要
本文提出了一个自我监督的目标，用于将对象定位在遮挡下 - 一种被称为对象永久性的属性。一个核心问题是在全部阻塞的情况下选择学习信号。我们没有直接监督看不见的对象的位置，而是提出一个不需要人类注释的自制目标，也不需要对对象动态的假设。我们表明，对象永久性可以通过优化内存的时间连贯性来浮出水面：我们沿着记忆的时空图表，每个时间步骤中的状态都是序列编码器中的非马克维亚特征。这导致了存储表示对象的存储表示形式，并预测其运动，以更好地定位它们。最终的模型在数个复杂性和现实主义的数据集上的现有方法优于现有方法，尽管需要最少的监督，从而广泛适用。
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence. A central question is the choice of learning signal in cases of total occlusion. Rather than directly supervising the locations of invisible objects, we propose a self-supervised objective that requires neither human annotation, nor assumptions about object dynamics. We show that object permanence can emerge by optimizing for temporal coherence of memory: we fit a Markov walk along a space-time graph of memories, where the states in each time step are non-Markovian features from a sequence encoder. This leads to a memory representation that stores occluded objects and predicts their motion, to better localize them. The resulting model outperforms existing approaches on several datasets of increasing complexity and realism, despite requiring minimal supervision, and hence being broadly applicable.
