RMNet

TL;DR: RMNet segments video objects by local-to-local matching, memorizing only the regions where the target appeared in past frames and tracking the query region with optical flow, which reduces both similar-object mismatching and computational cost.

Abstract

Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between the current and past frames, which lead to mismatching to similar objects and high computational complexity. To address these problems, we propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet). In RMNet, the precise regional memory is constructed by memorizing local regions where the target objects appear in the past frames. For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame. The proposed local-to-local matching effectively alleviates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets.

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BibTeX Copy

@inproceedings{xie2021efficient,
  title={Efficient Regional Memory Network for Video Object Segmentation},
  author={Xie, Haozhe and
          Yao, Hongxun and
          Zhou, Shangchen and
          Zhang, Shengping and
          Sun, Wenxiu},
  booktitle={CVPR},
  year={2021}
}