  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.
