Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement

Understand

We propose a new matching-based framework for semi-supervised video object segmentation (VOS).

• Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification.
• However, how to effectively organize information in the continuously growing feature bank remains under-explored, and this leads to inefficient design of the bank.
• We introduce an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features.