TIVE: A toolbox for identifying video instance segmentation errors.

• A error analyzing toolbox for VIS is proposed, bining false prediction into 7 types. • TIVE weights error type's damage to mAP and evaluates over different temporal length. • We investigate the spatial and temporal localization abilities of popular VIS models. In this paper, we introduce TIVE, a T oolbox for I dentifying V ideo instance segmentation E rrors. 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. [ABSTRACT FROM AUTHOR]