Perception of the visually disjoint surfaces of our cluttered world as whole
objects, physically distinct from those overlapping them, is a cognitive
phenomenon called objectness that forms the basis of our visual perception.
Shared by all vertebrates and present at birth in humans, it enables
object-centric representation and reasoning about the visual world. We present
a computational approach to objectness that leverages motion cues and
spatio-temporal attention using a pair of supervised spatio-temporal
R(2+1)U-Nets. The first network detects motion boundaries and classifies the
pixels at those boundaries in terms of their local foreground-background sense.
This motion boundary sense (MBS) information is passed, along with a
spatio-temporal object attention cue, to an attentional surface perception
(ASP) module which infers the form of the attended object over a sequence of
frames and classifies its 'pixels' as visible or obscured. The spatial form of
the attention cue is flexible, but it must loosely track the attended object
which need not be visible. We demonstrate the ability of this simple but novel
approach to infer objectness from phenomenology without object models, and show
that it delivers robust perception of individual attended objects in cluttered
scenes, even with blur and camera shake. We show that our data diversity and
augmentation minimizes bias and facilitates transfer to real video. Finally, we
describe how this computational objectness capability can grow in
sophistication and anchor a robust modular video object perception framework.

通过计算方法，我们展示了利用运动线索和时空注意力来推断客观性而不需要对象模型的能力，并展示了如何在混乱的场景中稳健地感知个别被关注的对象，甚至在模糊和相机抖动的情况下。我们展示了数据多样性和扩增的作用，以最小化偏差并便于应用于真实视频，并描述了如何进一步提升这种计算对象性能力，从而构建一个稳健的模块化视频对象感知框架。