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TBN-ViT: Temporal Bilateral Network with Vision Transformer for Video Scene Parsing

2021-12-02 07:53:36
Bo Yan, Leilei Cao, Hongbin Wang
arXiv_CV
       
Abstract

Video scene parsing in the wild with diverse scenarios is a challenging and great significance task, especially with the rapid development of automatic driving technique. The dataset Video Scene Parsing in the Wild(VSPW) contains well-trimmed long-temporal, dense annotation and high resolution clips. Based on VSPW, we design a Temporal Bilateral Network with Vision Transformer. We first design a spatial path with convolutions to generate low level features which can preserve the spatial information. Meanwhile, a context path with vision transformer is employed to obtain sufficient context information. Furthermore, a temporal context module is designed to harness the inter-frames contextual information. Finally, the proposed method can achieve the mean intersection over union(mIoU) of 49.85\% for the VSPW2021 Challenge test dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2112.01033

PDF

https://arxiv.org/pdf/2112.01033.pdf

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