Modeling temporal visual context across frames is critical for video instance segmentation (VIS) and other video understanding tasks. In this paper, we propose a fast online VIS model named CrossVIS. For temporal information modeling in VIS, we present a novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames. Different from previous schemes, crossover learning does not require any additional network parameters for feature enhancement. By integrating with the instance segmentation loss, crossover learning enables efficient cross-frame instance-to-pixel relation learning and brings cost-free improvement during inference. Besides, a global balanced instance embedding branch is proposed for more accurate and more stable online instance association. We conduct extensive experiments on three challenging VIS benchmarks, \ie, YouTube-VIS-2019, OVIS, and YouTube-VIS-2021 to evaluate our methods. To our knowledge, CrossVIS achieves state-of-the-art performance among all online VIS methods and shows a decent trade-off between latency and accuracy. Code will be available to facilitate future research.

本文提出了一种快速的在线视频实例分割模型CrossVIS，通过交叉学习方案，即使用当前帧中的实例特征来像素级定位其他帧中的相同实例，实现了跨帧实例到像素关系的学习，结合实例分割损失，提高了在线实例相关性的准确性和稳定性，在三个具有挑战性的VIS基准测试数据集上实现了最先进的性能。

快速在线视频实例分割的交叉学习