482 citations · 604 across the 16 of their papers we have counts for
6 papers · 1 filter
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model
Takehiro Aoshima, Takashi Matsubara
Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high…
ChartPointFlow for Topology-Aware 3D Point Cloud Generation
Takumi Kimura, Takashi Matsubara, Kuniaki Uehara
A point cloud serves as a representation of the surface of a three-dimensional (3D) shape. Deep generative models have been adapted to model their variations typically using a map…
Target-Oriented Deformation of Visual-Semantic Embedding Space
Takashi Matsubara
Multimodal embedding is a crucial research topic for cross-modal understanding, data mining, and translation. Many studies have attempted to extract representations from given enti…
Exploring Uncertainty Measures for Image-Caption Embedding-and-Retrieval Task
Kenta Hama, Takashi Matsubara, Kuniaki Uehara +1
With the wide development of black-box machine learning algorithms, particularly deep neural network (DNN), the practical demand for the reliability assessment is rapidly rising. O…
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data a…
A Novel Weight-Shared Multi-Stage CNN for Scale Robustness
Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures…