The task of video object segmentation is a fundamental but challenging problem in the field of computer vision. To deal with large variations in target objects and background clutter, we propose an online adaptive video object segmentation (VOS) framework, named Meta-VOS, that learns to adapt the target-specific segmentation. Meta-VOS builds an online adaptive learning process by exploiting cumulative expertise after searching for confidence patterns across different videos/frames, and then dynamically improves the model learning from two aspects: Meta-seg learner (i.e., module updating) and Meta-seg criterion (i.e., rule of expertise). As our goal is to rapidly determine which patterns best represent the essential characteristics of specific targets in a video, Meta-seg learner is introduced to adaptively learn to update the parameters and hyperparameters of segmentation network in very few gradient descent steps. Furthermore, a Meta-seg criterion of learned expertise, which is constructed to evaluate the Meta-seg learner for the online adaptation of the segmentation network, can confidently online update positive/negative patterns under the guidance of motion cues, object appearances and learned knowledge. Comprehensive evaluations on several benchmark datasets demonstrate the superiority of our proposed Meta-VOS when compared with other state-of-the-art methods applied to the VOS problem.

中文翻译：

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Meta-VOS：学习适应在线目标特定细分

视频对象分割的任务是计算机视觉领域中的一个基本但具有挑战性的问题。为了处理目标对象和背景杂波的巨大差异，我们提出了一种名为Meta-VOS的在线自适应视频对象分割（VOS）框架，该框架可学习以适应特定于目标的分割。Meta-VOS通过在不同视频/帧之间搜索置信度模式后利用累积的专业知识来构建在线自适应学习过程，然后从两个方面动态改进模型学习：元细分学习者（即模块更新）和元细分标准（即专业规则）。由于我们的目标是快速确定哪种模式最能代表视频中特定目标的基本特征，引入元段学习器以极少的梯度下降步骤自适应地学习更新分段网络的参数和超参数。此外，用于评估分段网络在线适应性的Meta-seg学习者的元专业学习元标准可以在运动线索，物体外观和学习的知识的指导下自信地在线更新正/负模式。 。与应用于VOS问题的其他最新方法相比，对多个基准数据集的综合评估证明了我们提出的Meta-VOS的优越性。用于评估Meta-seg学习者对分割网络的在线适应性的工具可以在运动线索，对象外观和所学知识的指导下自信地在线更新正/负模式。与应用于VOS问题的其他最新方法相比，对多个基准数据集的综合评估证明了我们提出的Meta-VOS的优越性。用于评估Meta-seg学习者对分割网络的在线适应性的工具可以在运动线索，对象外观和所学知识的指导下自信地在线更新正/负模式。与应用于VOS问题的其他最新方法相比，对多个基准数据集的综合评估证明了我们提出的Meta-VOS的优越性。