To the defence of online models for segmenting video instances

Madhu Jain, Venkata Ravi Kiran Kolla

Abstract


Online models gradually drew less attention, maybe as a result of their subpar performance, as offline models increasingly progressed video instance segmentation (VIS) in recent years. To handle lengthy video sequences and continuous movies, however, online approaches have an inherent advantage over offline models because to the computing resource limitations. Therefore, it would be extremely ideal if online models could perform equally well as offline models, if not better. We show that the performance disparity is mostly due to the error-prone association between frames induced by the similar appearance across various occurrences in the feature space by deconstructing the present online models and offline models. In light of this, we suggest an online contrastive learning paradigm that might help people learn more.

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