Group-wise Deep Object Co-Segmentation with Co-Attention Recurrent Neural Network

Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for real-world co-segmentation. This paper proposes a novel end-to-end deep learning approach for group-wise object co-segmentation with a recurrent network architecture. Specifically, the semantic features extracted from a pre-trained CNN of each image are first processed by single image representation branch to learn the unique properties. Meanwhile, a specially designed Co-Attention Recurrent Unit (CARU) recurrently explores all images to generate the final group representation by using the co-attention between images, and simultaneously suppresses noisy information. The group feature which contains synergetic information is broadcasted to each individual image and fused with multi-scale fine-resolution features to facilitate the inferring of co-segmentation. Moreover, we propose a group-wise training objective to utilize the co-object similarity and figure-ground distinctness as the additional supervision. The whole modules are collaboratively optimized in an end-to-end manner, further improving the robustness of the approach. Comprehensive experiments on three benchmarks can demonstrate the superiority of our approach in comparison with the state-of-the-art methods.