  Estimating dense correspondences between images is a long-standing image
under-standing task. Recent works introduce convolutional neural networks
(CNNs) to extract high-level feature maps and find correspondences through
feature matching. However,high-level feature maps are in low spatial resolution
and therefore insufficient to provide accurate and fine-grained features to
distinguish intra-class variations for correspondence matching. To address this
problem, we generate robust features by dynamically selecting features at
different scales. To resolve two critical issues in feature selection,i.e.,how
many and which scales of features to be selected, we frame the feature
selection process as a sequential Markov decision-making process (MDP) and
introduce an optimal selection strategy using reinforcement learning (RL). We
define an RL environment for image matching in which each individual action
either requires new features or terminates the selection episode by referring a
matching score. Deep neural networks are incorporated into our method and
trained for decision making. Experimental results show that our method achieves
comparable/superior performance with state-of-the-art methods on three
benchmarks, demonstrating the effectiveness of our feature selection strategy.
