RANet: Ranking Attention Network for Fast Video Object Segmentation
Despite online learning (OL) techniques have boosted the performance of semi supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restricts their practicality. Matching based and propagation based methods run at a faster speed by avoiding OL techniques. However, they are limited by sub optimal accuracy, due to mismatching and drifting problems. In this paper, we develop a real time yet very accurate Ranking Attention Network (RANet) for VOS. Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder decoder framework to learn pixel level similarity and segmentation in an end to end manner. To better utilize the similarity maps, we propose a novel ranking attention module, which automatically ranks and selects these maps for fine grained VOS performance. Experiments on DAVIS16 and DAVIS17 datasets show that our RANet achieves the best speed accuracy trade off, e.g. with 33 milliseconds per frame and J&F=85.5% on DAVIS16. With OL, our RANet reaches J&F=87.1% on DAVIS16, exceeding state of the art VOS methods. The code can be found at https:/github.com/Storife/RANet.
2019 IEEE/CVF International Conference on Computer Vision (ICCV)