DVIS-DAQ: Improving Video Segmentation via Dynamic Anchor Queries
CoRR(2024)
Abstract
Modern video segmentation methods adopt object queries to perform inter-frame
association and demonstrate satisfactory performance in tracking continuously
appearing objects despite large-scale motion and transient occlusion. However,
they all underperform on newly emerging and disappearing objects that are
common in the real world because they attempt to model object emergence and
disappearance through feature transitions between background and foreground
queries that have significant feature gaps. We introduce Dynamic Anchor Queries
(DAQ) to shorten the transition gap between the anchor and target queries by
dynamically generating anchor queries based on the features of potential
candidates. Furthermore, we introduce a query-level object Emergence and
Disappearance Simulation (EDS) strategy, which unleashes DAQ's potential
without any additional cost. Finally, we combine our proposed DAQ and EDS with
DVIS to obtain DVIS-DAQ. Extensive experiments demonstrate that DVIS-DAQ
achieves a new state-of-the-art (SOTA) performance on five mainstream video
segmentation benchmarks. Code and models are available at
.
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