  In this paper, we propose a novel fully convolutional two-stream fusion
network (FCTSFN) for interactive image segmentation. The proposed network
includes two sub-networks: a two-stream late fusion network (TSLFN) that
predicts the foreground at a reduced resolution, and a multi-scale refining
network (MSRN) that refines the foreground at full resolution. The TSLFN
includes two distinct deep streams followed by a fusion network. The intuition
is that, since user interactions are more direct information on
foreground/background than the image itself, the two-stream structure of the
TSLFN reduces the number of layers between the pure user interaction features
and the network output, allowing the user interactions to have a more direct
impact on the segmentation result. The MSRN fuses the features from different
layers of TSLFN with different scales, in order to seek the local to global
information on the foreground to refine the segmentation result at full
resolution. We conduct comprehensive experiments on four benchmark datasets.
The results show that the proposed network achieves competitive performance
compared to current state-of-the-art interactive image segmentation methods
