Fig. 2

The overall architecture of our proposed method. It is composed of three major parts: a feature encoder for encoding features at different abstract levels, an SBox-Net for initial segmentation and a Click-Net for refinement. Using the feature encoder, we obtain feature maps at two levels of abstraction, namely, low-level features and high-level features. Our SBox-Net is used to predict segmentation at a coarse level; thus, we highlight our high-level features by reducing the number of channels of low-level feature maps. In addition, in our Click-Net, we reduce the channels of high-level features since our goal is to recover details according to user clicks. All previously mentioned channel reduction operations are performed by 1*1 convolution. Finally, we simulate user clicks by sampling from differences of SBox-Net segmentation and the ground truth (denoted by ⊗ in the figure)