Image Co-segmentation with Multi-Scale Dual-Cross Correlation Network

Considering that the global correlation between images is very important for image co-segmentation, we propose a multi-scale Dual-Cross Correlation Network (DCNet) that can efficiently capture global matching information across images to obtain segmentation results. Specifically, the low-dimensional index feature is used to calculate the correlation and the high-dimensional content features are combined with the correlation matrix for final segmentation. Meanwhile, we specially design a Dual-Cross Correlation Module (DCCM) which harvests the spatial and channel correlation with the adjacent pixels of another image on the cross path to enhance the representation of correlation efficiently. By utilizing a further loop operation, each feature can capture the global dependencies from all pixels of another feature. Furthermore, we fuse multi-scale correlation and features into the decoder, which is called Multi-scale Correlation Fusing Decoder (MCFD), to refine the final segmentation results. Moreover, we introduce a new dice loss function to train the whole network by averaging the dice loss value of the foreground and background. Finally, we validate our method on three co-segmentation benchmarks and the results show that our method achieves the state-of-the-art performance.