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- W4386590498 abstract "Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance. In this work, we identify three key aspects where we can improve such methods; i) supervisory signal, ii) pretraining and iii) spatial awareness. We then propose TrickVOS; a generic, method-agnostic bag of tricks addressing each aspect with i) a structure-aware hybrid loss, ii) a simple decoder pretraining regime and iii) a cheap tracker that imposes spatial constraints in model predictions. Finally, we propose a lightweight network and show that when trained with TrickVOS, it achieves competitive results to state-of-the-art methods on DAVIS and YouTube benchmarks, while being one of the first STM-based SVOS methods that can run in real-time on a mobile device." @default.
- W4386590498 created "2023-09-11" @default.
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- W4386590498 date "2023-10-08" @default.
- W4386590498 modified "2023-09-30" @default.
- W4386590498 title "TRICKVOS: A Bag of Tricks for Video Object Segmentation" @default.
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- W4386590498 doi "https://doi.org/10.1109/icip49359.2023.10222050" @default.
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