SA-VIS: Sparse frame Annotations for training Video Instance Segmentation
Recent online video instance segmentation (VIS) methods have achieved impressive results, thus becoming the preferred approach to segment instances in videos. Despite the resurgence of impressive single image models, the online (or semi-online) VIS approaches outperform single-image models (e.g., based on SAM) by using long sequences of densely annotated frames during training. However,such a training setup of VIS is expensive in the sense of compute as well as dense annotations required. In order to solve these major flaws, we argue that the effective modeling of the instances and their evolution in videos do not require densely annotated frames. To that end, we propose a simple and effe...
| Authors | Edoardo Mello Rella, Ajad Chhatkuli, Shipra Jain et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 29 June 2026 |
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