Agss-Vos: Attention Guided Single-Shot Video Object Segmentation
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)(2019)
摘要
Most video object segmentation approaches process objects separately. This incurs high computational cost when multiple objects exist. In this paper, we propose AGSS-VOS to segment multiple objects in one feed-forward path via instance-agnostic and instance-specific modules. Information from the two modules is fused via an attention-guided decoder to simultaneously segment all object instances in one path. The whole framework is end-to-end trainable with instance IoU loss. Experimental results on Youtube-VOS and DAVIS-2017 dataset demonstrate that AGSS-VOS achieves competitive results in terms of both accuracy and efficiency.
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关键词
object instances,instance IoU loss,Youtube- VOS,AGSS-VOS,high computational cost,feed-forward path,instance-specific modules,attention-guided decoder,attention guided single-shot video object segmentation,instance-agnostic modules,end-to-end trainable
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