Recent compositional scene representation learning models have become
remarkably good in segmenting and tracking distinct objects within visual
scenes. Yet, many of these models require that objects are continuously, at
least partially, visible. Moreover, they tend to fail on intuitive physics
tests, which infants learn to solve over the first months of their life. Our
goal is to advance compositional scene representation algorithms with an
embedded algorithm that fosters the progressive learning of intuitive physics,
akin to infant development. As a fundamental component for such an algorithm,
we introduce Loci-Looped, which advances a recently published unsupervised
object location, identification, and tracking neural network architecture
(Loci, Traub et al., ICLR 2023) with an internal processing loop. The loop is
designed to adaptively blend pixel-space information with anticipations
yielding information-fused activities as percepts. Moreover, it is designed to
learn compositional representations of both individual object dynamics and
between-objects interaction dynamics. We show that Loci-Looped learns to track
objects through extended periods of object occlusions, indeed simulating their
hidden trajectories and anticipating their reappearance, without the need for
an explicit history buffer. We even find that Loci-Looped surpasses
state-of-the-art models on the ADEPT and the CLEVRER dataset, when confronted
with object occlusions or temporary sensory data interruptions. This indicates
that Loci-Looped is able to learn the physical concepts of object permanence
and inertia in a fully unsupervised emergent manner. We believe that even
further architectural advancements of the internal loop - also in other
compositional scene representation learning models - can be developed in the
near future.

通过引入具有内部处理循环的 Loci-Looped 算法，本研究旨在推进组合场景表示算法，促进基于婴儿发展类似的直觉物理学习，以实现在对象遮挡或临时感官数据中断时无需显式历史缓冲区即能跟踪物体，以及以完全无监督的涌现方式学习对象永恒性和惯性这两个物理概念。

LOCI 循环：从视频中发展物体恒常性

Looping LOCI: Developing Object Permanence from Videos

In this work, we present an end-to-end network for stereo-consistent image
inpainting with the objective of inpainting large missing regions behind
objects. The proposed model consists of an edge-guided UNet-like network using
Partial Convolutions. We enforce multi-view stereo consistency by introducing a
disparity loss. More importantly, we develop a training scheme where the model
is learned from realistic stereo masks representing object occlusions, instead
of the more common random masks. The technique is trained in a supervised way.
Our evaluation shows competitive results compared to previous state-of-the-art
techniques.

本研究提出了一种端到端的网络，旨在利用基于 Partial Convolutions 的边缘引导 U-Net 网络填充大面积部分，通过引入视差损失实现多视图立体一致性，采用真实立体遮罩训练，结果显示与之前的技术相比有一定的竞争优势。