Do many types of video segmentation with one model without retraining with TarViS
Do many types of video segmentation with one model without retraining with TarViS
TarViS: A Unified Approach for Target-based Video Segmentation
arXiv paper abstract https://arxiv.org/abs/2301.02657
arXiv PDF paper https://arxiv.org/pdf/2301.02657.pdf
... video segmentation is currently fragmented into different tasks spanning multiple benchmarks ... methods are overwhelmingly task-specific and cannot conceptually generalize to other tasks.
... propose TarViS: a novel, unified network architecture that can be applied to any task that requires segmenting a set of arbitrarily defined 'targets' in video.
... approach is flexible with respect to how tasks define these targets, since it models the latter as abstract 'queries' which are then used to predict pixel-precise target masks.
A single TarViS model can be trained jointly on a collection of datasets spanning different tasks, and can hot-swap between tasks during inference without any task-specific retraining.
... apply TarViS to four different tasks, namely Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), Video Object Segmentation (VOS) and Point Exemplar-guided Tracking (PET).
... unified, jointly trained model achieves state-of-the-art performance on 5/7 benchmarks spanning these four tasks, and competitive performance on the remaining two.
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