Tree Hierarchical Cnns For Object Parsing

Object parsing is a challenging topic in computer vision, which is to distinguish all parts of visual objects. Although lots of works have been proposed, it is difficult to segment complicated objects from complex scenes. Therefore, in this paper we propose a tree hierarchical CNNs for object parsing. Rather than segment all parts of objects at once, we segment object parts step by step in a tree hierarchy and then merge the results together with a full convolutional network. In the tree hierarchy, the segmentation errors of the previous layers of the network outputs could be passed down to following layers and result in accumulated errors. In order to reduce the accumulated errors, we adopt a new part-aware fusion strategy, which fuses global-level feature maps from fully convolutional networks as well as the part-level object feature maps from the output of previous layer. It also contributes to improve the integrity and robustness of object parsing. Finally, the experiments on published datasets show the superiority of the proposed approach, especially for neighboring objects in complex scene.