Interactive Video Object Segmentation

Using Global and Local Transfer Modules

Yuk Heo
Korea University

Yeong Jun Koh
Chungnam National University

Chang-Su Kim
Korea University


An interactive video object segmentation algorithm, which takes scribble annotations on query objects as input, is proposed in this paper. We develop a deep neural network, which consists of the annotation network (A-Net) and the transfer network (T-Net). First, given user scribbles on a frame, A-Net yields a segmentation result based on the encoder-decoder architecture. Second, T-Net transfers the segmentation result bidirectionally to the other frames, by employing the global and local transfer modules. The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame. By applying A-Net and T-Net alternately, a user can obtain desired segmentation results with minimal efforts. We train the entire network in two stages, by emulating user scribbles and employing an auxiliary loss. Experimental results demonstrate that the proposed interactive video object segmentation algorithm outperforms the state-of-the-art conventional algorithms.

Overall Network

Overall Process

Real-world evaluation


Yuk Heo, Yeong Jun Koh, and Chang-Su Kim, "Interactive Video Object Segmentation Using Global and Local Transfer Modules," accepted to Proceedings of European Conference on Computer Vision (ECCV), 2020.
[arXiv] [code]