We present a novel fusion scheme between multiple intermediate convolutional features within convolutional neurual network (CNN) for dense correspondence estimation. In contrast to existing CNN-based descriptors that utilize a single convolutional activation, our approach jointly uses multiple intermediate features of CNN through the attention weight that balances the contribution of each features. We formulate the overall network as two sub-networks, correspondence network and attention network. The correspondence network is designed to provide multiple intermediate matching costs while the attention network is to learn the optimal weight between them. These two networks are learned in a joint manner to boost the correspondence estimation performance. Experiments demonstrate that our proposed method outperforms the state-of-the-art methods on various correspondence estimation tasks including depth estimation, optical flow, and semantic correspondence.