Learning effective representations from unlabeled data is a challenging task for point cloud
understanding. As the human visual system can map concepts learned from 2D images to …

In computer vision fields, 3D object recognition is one of the most important tasks for many
real-world applications. Three-dimensional convolutional neural networks (CNNs) have …

Object detection is an important topic for visual data processing in the visual computing
area. Although a number of approaches have been studied, it still remains a challenge …

In the deep-learning community, new algorithms are published at a very fast pace.
Therefore, solving an image classification problem for new datasets becomes a challenging …

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved
drug target for the treatment of a specific disease. It has received extensive attention …

Current supervised methods for 3D shape representation learning have achieved satisfying
performance, yet require extensive human-labeled datasets. Unsupervised learning-based …

Let D be a dataset of smooth 3D surfaces, partitioned into disjoint classes CL j, j= 1,…, k. We
show how optimized diffeomorphic registration applied to large numbers of pairs (S, S′), S …

Convolutional Neural Networks (CNNs) have been widely used for 3D shape recognition.
Despite its significant performance, we point out that there is still some rooms for …

Abstract 3D shape completion from single-view scan is an important task for follow-up
applications such as recognition and segmentation, but it is challenging due to the critical …

In this paper, a novel model is proposed to segment image instance based on fractional-
order chaotic synchronization network and reinforcement learning method. In the proposed …