Episodic CAMN: Contextual Attention-based Memory Networks
With Iterative Feedback For Scene Labeling
Abstract
Scene labeling can be seen as a sequence-sequence prediction task (pixels-labels), and it is quite important to
leverage relevant context to enhance the performance of
pixel classification. In this paper, we introduce an episodic
attention-based memory network to achieve the goal. We
present a unified framework that mainly consists of a Convolutional Neural Network (CNN), specifically, Fully Convolutional Network (FCN) and an attention-based memory
module with feedback connections to perform context selection and refinement. The full model produces contextaware representation for each target patch by aggregating
the activated context and its original local representation
produced by the convolution layers. We evaluate our model
on PASCAL Context, SIFT Flow and PASCAL VOC 2011
datasets and achieve competitive results to other state-ofthe-art methods in scene labeling.