An End-to-End Tree Based Approach for Instance Segmentation

This paper presents an approach for bottom-up hierarchical instance segmentation. We propose an end-to-end model to estimate energies of regions in an hierarchical region tree. To this end, we introduce a Convolutional Tree-LSTM module to leverage the tree-structured network topology. For constructing the hierarchical region tree, we utilize the accurate boundaries predicted from a pre-trained convolutional oriented boundary network. We evaluate our model on PASCAL VOC 2012 dataset showing that we obtain good trade-off between segmentation accuracy and time taken to process a single image.