Recent works have shown that objects discovery can largely benefit from the
inherent motion information in video data. However, these methods lack a proper
background processing, resulting in an over-segmentation of the non-object
regions into random segments. This is a critical limitation given the
unsupervised setting, where object segments and noise are not distinguishable.
To address this limitation we propose BMOD, a Background-aware Motion-guided
Objects Discovery method. Concretely, we leverage masks of moving objects
extracted from optical flow and design a learning mechanism to extend them to
the true foreground composed of both moving and static objects. The background,
a complementary concept of the learned foreground class, is then isolated in
the object discovery process. This enables a joint learning of the objects
discovery task and the object/non-object separation. The conducted experiments
on synthetic and real-world datasets show that integrating our background
handling with various cutting-edge methods brings each time a considerable
improvement. Specifically, we improve the objects discovery performance with a
large margin, while establishing a strong baseline for object/non-object
separation.

提出了一种背景感知的运动引导对象发现方法，通过利用光流提取的运动对象的遮罩并设计学习机制将其扩展到由移动和静止对象组成的真实前景，并在对象发现过程中隔离背景，从而实现对象发现任务和对象 / 非对象分离的联合学习，为对象发现性能和对象 / 非对象分离建立了强有力的基线。