Adaptive Guidance and Attention-Refined Network for Fast Video Object Segmentation

Most video object segmentation networks have difficulties in balancing accuracy and speed, leading them to fail to meet the requirements of application. In this paper, we propose a lightweight online-trained video object segmentation network. Specifically, to force the network focus on the potential object, we propose a new way to guide the encoder module by classification score map, and integrate a cross-dimension attention into the refinement segmentation module. Meanwhile, to reduce the negative influence of unreliable samples, we use two indexes to adaptively choose templates for the memory module. Experiments were conducted on three popular benchmarks, and our approach has achieved a good trade-off between accuracy and speed.