GetNet: Get Target Area for Image Pairing Image pairing is an important research task in the field of computer vision. And finding image pairs containing objects of the same category is the basis of many tasks such as tracking and person reidentification, etc., and it is also the focus of our research. Existing traditional methods and deep learningbased methods have some degree of defects in speed or accuracy. In this paper, we made improvements on the Siamese network 1 and proposed GetNet. The proposed method GetNet combines STN 2 and Siamese network to get the target area first and then perform subsequent processing. Experiments show that our method achieves competitive results in speed and accuracy.

A TwoStream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a twostream deep network. By internally augmenting the target image, the network considers the twostream with the three input images and reflects the additional augmented pair in the training. As a result, the training process of the deep network is regularized and the network becomes robust for the variance of aerial images. Furthermore, we introduce an ensemble method that is based on the bidirectional network, which is motivated by the isomorphic nature of the geometric transformation. We obtain two global transformation parameters without any additional network or parameters, which alleviate asymmetric matching results and enable significant improvement in performance by fusing two outcomes. For the experiment, we adopt aerial images from Google Earth and the International Society for Photogrammetry and Remote Sensing (ISPRS). To quantitatively assess our result, we apply the probability of correct keypoints (PCK) metric, which measures the degree of matching. The qualitative and quantitative results show the sizable gap of performance compared to the conventional methods for matching the aerial images. All code and our trained model, as well as the dataset are available online.