In this paper, we propose a matching network for gradually estimating the geometric transformation parameters between two aerial images taken in the same area but in different environments. To precisely matching two a...
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In this paper, we propose a matching network for gradually estimating the geometric transformation parameters between two aerial images taken in the same area but in different environments. To precisely matching two aerial images, there are important factors to consider such as different time, a variation of viewpoint, size, and rotation. The conventional methods for matching aerial image pairs with the large variations are extremely time-consuming process and have the limitations finding correct correspondences, because the image gradient and grayscale intensity for generating the feature descriptors are not robust to the variations. We design the network architecture as an end-to-end trainable deep neural network to reflect the characteristics of aerial images. The hierarchical structures that orderly estimate the rotation and the affine transformations make it possible to reduce the range of predictions and minimize errors caused by misalignment, resulting in more precise matching performance. Furthermore, we apply transfer learning to make the feature extraction networks more robust and suitable for the aerial image domain with the large variations. For the experiment, we apply the remote sensing image datasets from Google Earth and International Society for Photogrammetry and Remote Sensing (ISPRS). To evaluate our method quantitatively, we measure the probability of correct keypoints (PCK) metrics for objectively comparing the degree of matching. In terms of qualitative and quantitative assessment, our method demonstrates the state-of-the-art performances compared to the existing methods.
With the recent development of sequence-to-sequence framework, generation approach for short text conversation becomes attractive. Traditional sequence-to-sequence method for short text conversation often suffers from...
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This study proposes a novel all-neural approach for multichannel speech enhancement, where robust speaker localization, acoustic beamforming, post-filtering and spatial filtering are all done using deep learning based...
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Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and...
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作者：
Chen, KuanChen, BoLai, JiahaoYu, KaiKey Lab. of Shanghai Education
Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China
Waveform generator is a key component in voice conversion. Recently, WaveNet waveform generator conditioned on the Mel-cepstrum (Mcep) has shown better quality over standard vocoder. In this paper, an enhanced WaveNet...
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This work proposes a new learning framework that uses a loss function in the frequency domain to train a convolutional neural network (CNN) in the time domain. At the training time, an extra operation is added after t...
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Many real-world applications of speech enhancement, such as hearing aids and cochlear implants, desire real-time processing, with no or low latency. In this paper, we propose a novel convolutional recurrent network (C...
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Redox cofactors mediate many enzymatic processes and are increasingly employed in biomedical and energy applications. Exploring the influence of external magnetic fields on redox cofactor chemistry can enhance our und...
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Redox cofactors mediate many enzymatic processes and are increasingly employed in biomedical and energy applications. Exploring the influence of external magnetic fields on redox cofactor chemistry can enhance our understanding of magnetic-field-sensitive biological processes and allow the application of magnetic fields to modulate redox reactions involving cofactors. Through a combination of experiments and modeling, we investigate the influence of magnetic fields on electrochemical reactions in redox cofactor solutions. By employing flavin mononucleotide (FMN) cofactor as a model system, we characterize magnetically induced changes in Faradaic currents. We find that radical pair intermediates have negligible influence on current increases in FMN solution upon application of a magnetic field. The dominant mechanism underlying the observed current increases is the magneto-hydrodynamic effect. We extend our analyses to other diffusion-limited electrochemical reactions of redox cofactor solutions and arrive at similar conclusions, highlighting the opportunity to use this framework in redox cofactor chemistry.
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated th...
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In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD patients, and 2) their prescri...
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