论文标题
学习视频实例细分，并通过复发图神经网络
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
论文作者
论文摘要
视频实例细分的大多数现有方法都包含多个模块，这些模块是启发式组合以产生最终输出的。取而代之的是制定一种纯粹基于学习的方法，该方法既建模时间方面，又是解决视频实例细分任务所需的通用轨道管理，这是一个极具挑战性的问题。在这项工作中，我们提出了一种新颖的学习公式，其中整个视频实例分割问题是共同建模的。我们将一个灵活的模型适合我们的公式，借助图神经网络，在每个帧中处理所有可用的新信息。通过经常连接考虑过去的信息并处理。我们证明了拟议方法在综合实验中的有效性。我们的方法以超过25 fps运行，优于先前的视频实时方法。我们进一步进行了详细的消融实验，以验证方法的不同方面。
Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output. Formulating a purely learning-based method instead, which models both the temporal aspect as well as a generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning formulation, where the entire video instance segmentation problem is modelled jointly. We fit a flexible model to our formulation that, with the help of a graph neural network, processes all available new information in each frame. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach.
