Adaptive ROI generation for video object segmentation using reinforcement learning

The task of the proposed method is semi-supervised video object segmentation where only the ground-truth segmentation of the first frame is provided. The existing approaches rely on selecting the region of interest for model update; however it is rough and inflexible, leading to performance degradation. To overcome this limitation, a novel approach is proposed which utilizes reinforcement learning to select optimal adaptation areas for each frame, based on the historical segmentation information. The RL model learns to take optimal actions to adjust the region of interest inferred from the previous frame for online model updating. To speed up the model adaption, a novel multi-branch tree based exploration method is designed to quickly select the best state action pairs. The proposed method is evaluated on three common video object segmentation datasets including DAVIS 2016, SegTrack V2 and Youtube-Object. The results show that the proposed work improves the state-of-the-art of the mean region similarity to 87.1% on the DAVIS 2016 dataset, and to 79.5% on the Youtube-Object dataset. Meanwhile, competitive performance is obtained on the SegTrack V2 dataset. Code is at https://github.com/insomnia94/ARG.