Abstract
We address the problem of image segmentation from natural language descriptions. Existing deep learning-based
methods encode image representations based on the output
of the last convolutional layer. One general issue is that the
resulting image representation lacks multi-scale semantics,
which are key components in advanced segmentation systems. In this paper, we utilize the feature pyramids inherently existing in convolutional neural networks to capture
the semantics at different scales. To produce suitable information flow through the path of feature hierarchy, we propose Recurrent Refinement Network (RRN) that takes pyramidal features as input to refine the segmentation mask progressively. Experimental results on four available datasets
show that our approach outperforms multiple baselines and
state-of-the-art