This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative information fusion over video graphs. Specifically, AGNN builds a fully connected graph to efficiently represent frames as nodes, and relations between arbitrary frame pairs as edges. The underlying pair-wise relations are described by a differentiable attention mechanism. Through parametric message passing, AGNN is able to efficiently capture and mine much richer and higher-order relations between video frames, thus enabling a more complete understanding of video content and more accurate foreground estimation. Experimental results on three video segmentation datasets show that AGNN sets a new state-of-the-art in each case. To further demonstrate the generalizability of our framework, we extend AGNN to an additional task: image object co-segmentation (IOCS). We perform experiments on two famous IOCS datasets and observe again the superiority of our AGNN model. The extensive experiments verify that AGNN is able to learn the underlying semantic/appearance relationships among video frames or related images, and discover the common objects.

提出了一种新的关注图神经网络 (AGNN)，用于零样本视频对象分割 (ZVOS)。所提出的 AGNN 将此任务作为视频图上迭代信息融合的过程。通过参数化消息传递，AGNN 能够有效地捕捉和挖掘视频帧之间更丰富和更高阶的关系，因此实现更全面的视频内容理解和更准确的前景估计。扩展 AGNN 到另一个任务时，我们进一步证明了我们的框架的通用性。广泛的实验验证了 AGNN 能够学习视频帧或相关图像之间的潜在语义/外观关系，并发现共同的对象。

通过关注图神经网络进行零样本视频物体分割