A novel co-attention computation block for deep learning based image co-segmentation

The correlation between images is crucial for solving the image co-segmentation problem that is segmenting common and salient objects from a set of related images. This paper proposes a novel co-attention computation block to compute the visual correlation between images for improving the co-segmentation performance. Here 'co-attention' means that we obtain the co-attention features in encoded features of an image to guide the attention in another image. To this purpose, we firstly introduce top-k average pooling to compute the channel co-attention descriptor. Then we explore the correlation between features in different spatial positions to get the spatial co-attention descriptor. Finally, these two types of co-attention descriptors are multiplied to generate a fused one. We obtain such a fused co-attention descriptor for each image and use it to produce the co-attention augmented feature map for the following processing in the applications. We embed the proposed co-attention block into a U-shaped Siamese network for fulfilling the image co-segmentation. It is proven to be able to improve the performance effectively in the experiments. To our best knowledge, it leads to the currently best results on Internet dataset and iCoseg dataset. (C) 2020 Elsevier B.V. All rights reserved.