  Since first proposed, Video Instance Segmentation(VIS) task has attracted
vast researchers' focus on architecture modeling to boost performance. Though
great advances achieved in online and offline paradigms, there are still
insufficient means to identify model errors and distinguish discrepancies
between methods, as well approaches that correctly reflect models' performance
in recognizing object instances of various temporal lengths remain barely
available. More importantly, as the fundamental model abilities demanded by the
task, spatial segmentation and temporal association are still understudied in
both evaluation and interaction mechanisms. In this paper, we introduce TIVE, a
Toolbox for Identifying Video instance segmentation Errors. By directly
operating output prediction files, TIVE defines isolated error types and
weights each type's damage to mAP, for the purpose of distinguishing model
characters. By decomposing localization quality in spatial-temporal dimensions,
model's potential drawbacks on spatial segmentation and temporal association
can be revealed. TIVE can also report mAP over instance temporal length for
real applications. We conduct extensive experiments by the toolbox to further
illustrate how spatial segmentation and temporal association affect each other.
We expect the analysis of TIVE can give the researchers more insights, guiding
the community to promote more meaningful explorations for video instance
segmentation. The proposed toolbox is available at
https://github.com/wenhe-jia/TIVE.
