  Many video instance segmentation (VIS) methods partition a video sequence
into individual frames to detect and segment objects frame by frame. However,
such a frame-in frame-out (FiFo) pipeline is ineffective to exploit the
temporal information. Based on the fact that adjacent frames in a short clip
are highly coherent in content, we propose to extend the one-stage FiFo
framework to a clip-in clip-out (CiCo) one, which performs VIS clip by clip.
Specifically, we stack FPN features of all frames in a short video clip to
build a spatio-temporal feature cube, and replace the 2D conv layers in the
prediction heads and the mask branch with 3D conv layers, forming clip-level
prediction heads (CPH) and clip-level mask heads (CMH). Then the clip-level
masks of an instance can be generated by feeding its box-level predictions from
CPH and clip-level features from CMH into a small fully convolutional network.
A clip-level segmentation loss is proposed to ensure that the generated
instance masks are temporally coherent in the clip. The proposed CiCo strategy
is free of inter-frame alignment, and can be easily embedded into existing FiFo
based VIS approaches. To validate the generality and effectiveness of our CiCo
strategy, we apply it to two representative FiFo methods, Yolact
\cite{bolya2019yolact} and CondInst \cite{tian2020conditional}, resulting in
two new one-stage VIS models, namely CiCo-Yolact and CiCo-CondInst, which
achieve 37.1/37.3\%, 35.2/35.4\% and 17.2/18.0\% mask AP using the ResNet50
backbone, and 41.8/41.4\%, 38.0/38.9\% and 18.0/18.2\% mask AP using the Swin
Transformer tiny backbone on YouTube-VIS 2019, 2021 and OVIS valid sets,
respectively, recording new state-of-the-arts. Code and video demos of CiCo can
be found at \url{https://github.com/MinghanLi/CiCo}.
