In this paper, we propose a novel method to precisely match two aerial images that were obtained in different environments via a two-stream deep network. By internally augmenting the target image, the network considers the two-stream 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.

Several voice conversion (VC) methods using a simple autoencoder with a carefully designed information bottleneck have recently been studied. In general, they extract content information from a given speech through the information bottleneck between the encoder and the decoder, providing it to the decoder along with the target speaker information to generate the converted speech. However, their performance is highly dependent on the downsampling factor of an information bottleneck. In addition, such frame-by-frame conversion methods cannot convert speaking styles associated with the length of utterance, such as the duration. In this paper, we propose a novel duration controllable voice conversion (DCVC) model, which can transfer the speaking style and control the speed of the converted speech through a phoneme-based information bottleneck. The proposed information bottleneck does not need to find an appropriate downsampling factor, achieving a better audio quality and VC performance. In our experiments, DCVC outperformed the baseline models with a 3.78 MOS and a 3.83 similarity score. It can also smoothly control the speech duration while achieving a 39.35x speedup compared with a Seq2seq-based VC in terms of the inference speed.

Remote sensing image retrieval (RSIR) is the process of searching for identical areas by investigating the similarities between a query image and the database images. RSIR is a challenging task owing to the time difference, viewpoint, and coverage area depending on the shooting circumstance, resulting in variations in the image contents. In this paper, we propose a novel method based on a coarse-to-fine strategy, which makes a deep network more robust to the variations in remote sensing images. Moreover, we propose a new triangular loss function to consider the whole relation within the tuple. This loss function improves the retrieval performance and demonstrates better performance in terms of learning the detailed information in complex remote sensing images. To verify our methods, we experimented with the Google Earth South Korea dataset, which contains 40,000 images, using the evaluation metric Recall@n. In all experiments, we obtained better performance results than those of the existing retrieval training methods. Our source code and Google Earth South Korea dataset are available online.