2021
SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
CVPR 2021poster
In this paper we introduce a Transformer-based approach to video object segmentation (VOS). To address compounding error and scalability issues of prior work, we propose a scalable, end-to-end method for VOS called Sparse Spatiotemporal Transformers (SST). SST extracts per-pixel representations for…