Integrating human-provided location priors into video object segmentation has been shown to be an effective strategy to enhance performance, but their application at large scale is unfeasible. Gamification can help reduce the annotation burden, but it still requires user involvement. We propose a video object segmentation framework that leverages the combined advantages of user feedback for segmentation and gamification strategy by simulating multiple game players through a reinforcement learning (RL) model that reproduces human ability to pinpoint moving objects and using the simulated feedback to drive the decisions of a fully convolutional deep segmentation network. Experimental results on the DAVIS-17 benchmark show that: 1) including user-provided prior, even if not precise, yields high performance; 2) our RL agent replicates satisfactorily the same variability of humans in identifying spatiotemporal salient objects; and 3) employing artificially generated priors in an unsupervised video object segmentation model reaches state-of-the-art performance.

中文翻译：

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MASK-RL：通过强化学习的多主体视频对象细分框架。

已经证明将人类提供的位置先验整合到视频对象分割中是提高性能的有效策略，但是将其大规模应用是不可行的。游戏化可以帮助减轻注释负担，但仍需要用户参与。我们提出了一种视频对象细分框架，该方法通过增强学习（RL）模型来模拟多个游戏玩家，从而利用用户反馈的组合优势来进行细分和游戏化策略，该模型可再现人类精确定位移动对象的能力，并使用模拟反馈来驱动决策卷积深度细分网络的概念 在DAVIS-17基准测试中的实验结果表明：1）包括用户提供的即使不是精确的事先提供的结果，也会产生高性能；2）我们的RL代理在识别时空显着物体时可以令人满意地复制人类的相同变异性；和3）在无人监督的视频对象分割模型中采用人工生成的先验，可以达到最新的性能。