在杂乱世界中感知物体：基于视频运动的计算物体性
Seeing Objects in a Cluttered World: Computational Objectness from Motion in Video
AI总结：
本研究提出一种基于运动线索和时空注意力的计算物体性方法，采用双R(2+1)U-Net结构，无需物体模型即可在杂乱场景中鲁棒感知被关注物体，且能有效迁移至真实视频，为模块化视频物体感知框架提供基础。
摘要：
将杂乱世界中视觉上不连续的表面感知为完整的、与重叠物体物理上相区分的物体，是一种被称为物体性（objectness）的认知现象，它构成了人类视觉感知的基础。这种能力为所有脊椎动物共有，且人类在出生时就具备，它支撑了以物体为中心的表征和对视觉世界的推理。我们提出了一种计算物体性的方法，该方法利用运动线索和时空注意力，采用一对有监督的时空R(2+1)U-Net实现。第一个网络检测运动边界，并根据局部前景-背景属性对这些边界上的像素进行分类。该运动边界感知（Motion Boundary Sense，MBS）信息与时空物体注意力线索一同被输入注意力表面感知（Attentional Surface Perception，ASP）模块，该模块可推断连续帧中被关注物体的形态，并将其“像素”分类为可见或被遮挡。注意力线索的空间形态是灵活的，但它必须大致跟踪被关注的物体，而该物体不一定是可见的。我们证明了这种简单却新颖的方法能够在不依赖物体模型的情况下从现象学推断物体性，并且即使存在模糊和相机抖动，它也能在杂乱场景中对单个被关注物体实现鲁棒的感知。我们的实验表明，数据的多样性和增强策略最大限度地减少了偏差，并有助于模型向真实视频迁移。最后，我们阐述了这种计算物体性能力如何能够逐步提升复杂度，并为鲁棒的模块化视频物体感知框架提供支撑。
英文摘要：
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.
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