SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

• URL: http://arxiv.org/abs/2101.08833v1
• Date: Thu, 21 Jan 2021 20:06:12 GMT
• Title: SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation
• Authors: Brendan Duke and Abdalla Ahmed and Christian Wolf and Parham Aarabi and Graham W. Taylor
• Abstract summary: We introduce a Transformer-based approach to video object segmentation (VOS) Our attention-based approach allows a model to learn to attend over a history features of multiple frames. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness compared with the state of the art.
• Score: 24.884078497381633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 each object in a video using sparse attention over spatiotemporal features. Our attention-based formulation for VOS allows a model to learn to attend over a history of multiple frames and provides suitable inductive bias for performing correspondence-like computations necessary for solving motion segmentation. We demonstrate the effectiveness of attention-based over recurrent networks in the spatiotemporal domain. Our method achieves competitive results on YouTube-VOS and DAVIS 2017 with improved scalability and robustness to occlusions compared with the state of the art.

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