The ability to model the underlying dynamics of visual scenes and reason
about the future is central to human intelligence. Many attempts have been made
to empower intelligent systems with such physical understanding and prediction
abilities. However, most existing methods focus on pixel-to-pixel prediction,
which suffers from heavy computational costs while lacking a deep understanding
of the physical dynamics behind videos. Recently, object-centric prediction
methods have emerged and attracted increasing interest. Inspired by it, this
paper proposes an unsupervised object-centric prediction model that makes
future predictions by learning visual dynamics between objects. Our model
consists of two modules, perceptual, and dynamic module. The perceptual module
is utilized to decompose images into several objects and synthesize images with
a set of object-centric representations. The dynamic module fuses contextual
information, takes environment-object and object-object interaction into
account, and predicts the future trajectory of objects. Extensive experiments
are conducted to validate the effectiveness of the proposed method. Both
quantitative and qualitative experimental results demonstrate that our model
generates higher visual quality and more physically reliable predictions
compared to the state-of-the-art methods.

本研究提出了一种无监督的物体中心预测模型，通过学习物体之间的视觉动态来进行未来预测，实验结果表明该模型在生成视觉质量和物理可靠性方面优于现有技术。

物体中心视觉预测的物理动态学习

Learning Physical Dynamics for Object-centric Visual Prediction

We formulate a new problem as Object Importance Estimation (OIE) in on-road
driving videos, where the road users are considered as important objects if
they have influence on the control decision of the ego-vehicle's driver. The
importance of a road user depends on both its visual dynamics, e.g.,
appearance, motion and location, in the driving scene and the driving goal,
\emph{e.g}., the planned path, of the ego vehicle. We propose a novel framework
that incorporates both visual model and goal representation to conduct OIE. To
evaluate our framework, we collect an on-road driving dataset at traffic
intersections in the real world and conduct human-labeled annotation of the
important objects. Experimental results show that our goal-oriented method
outperforms baselines and has much more improvement on the left-turn and
right-turn scenarios. Furthermore, we explore the possibility of using object
importance for driving control prediction and demonstrate that binary brake
prediction can be improved with the information of object importance.

本文针对车载自动驾驶视觉场景中的对象重要性评估问题，提出了一种新的基于视觉动态和驾驶目标的对象重要性估计框架，并通过实际交通路口数据集及人工标注数据对该框架进行了验证评估。同时，本文还探讨了使用对象重要性进行驾驶控制预测的可能性，并证明了该方法可以有效提升车辆的刹车预测准确性。