Channel Interaction with Local Enhancement for Few-Shot Semantic Segmentation

State-of-the-art semantic segmentation algorithms are based on deep convolutional neural networks, which aim to exploit a great quantity of labeled samples to predict the class label for each pixel in an image. Few-shot segmentation alleviate the data labeling task, learning a model given only a few labeled samples, which adapts well to unseen classes. Previous works only extract spatial pixels relationship to build the guidance, which ignores the important feature channel information that contain abundant local semantic structure in the images. In this paper, we propose a novel channel interaction network (CINet) for few-shot semantic segmentation. It mainly consists of three novel steps: a local enhancement module that aggregates the spatial pixels connection in both query images and support images; a channel interaction module that captures the similar channel from relationship between each pair of images for segmentation guidance; and a residual attention module that exploiting residual connection of feature scales to alleviate spatial inconsistency between query images and corresponding example images. Extensive experiments on PASCAL-5idataset demonstrate that our model outperforms state-of-the-art methods without parameters increase sharply.