  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.
