  Object co-segmentation is the task of segmenting the same objects from
multiple images. In this paper, we propose the Attention Based Object
Co-Segmentation for object co-segmentation that utilize a novel attention
mechanism in the bottleneck layer of deep neural network for the selection of
semantically related features. Furthermore, we take the benefit of attention
learner and propose an algorithm to segment multi-input images in linear time
complexity. Experiment results demonstrate that our model achieves state of the
art performance on multiple datasets, with a significant reduction of
computational time.
