We introduce a method for simultaneously classifying, segmenting and tracking
object instances in a video sequence. Our method, named MaskProp, adapts the
popular Mask R-CNN to video by adding a mask propagation branch that propagates
frame-level object instance masks from each video frame to all the other frames
in a video clip. This allows our system to predict clip-level instance tracks
with respect to the object instances segmented in the middle frame of the clip.
Clip-level instance tracks generated densely for each frame in the sequence are
finally aggregated to produce video-level object instance segmentation and
classification. Our experiments demonstrate that our clip-level instance
segmentation makes our approach robust to motion blur and object occlusions in
video. MaskProp achieves the best reported accuracy on the YouTube-VIS dataset,
outperforming the ICCV 2019 video instance segmentation challenge winner
despite being much simpler and using orders of magnitude less labeled data
(1.3M vs 1B images and 860K vs 14M bounding boxes).

MaskProp 方法采用 Mask R-CNN 对视频中的物体实例进行同时分类、分割和跟踪，通过添加掩码传播分支来适应视频序列，并最终将产生的密集剪辑级实例跟踪汇总，以产生视频级物体实例分割和分类结果。