In recent years, point cloud representation has become one of the research hotspots in the
field of computer vision, and has been widely used in many fields, such as autonomous …

Abstract Machine learning has created new opportunities for data-intensive study in
interdisciplinary domains as a result of the advancement of big data technologies and high …

Radiance fields have demonstrated impressive performance in synthesizing novel views
from sparse input views yet prevailing methods suffer from high training costs and slow …

Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of
industrial machinery. Deep learning has been extensively investigated in fault diagnosis …

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based
architecture for talking portrait synthesis that can concurrently achieve fast convergence, real …

Recent advances in deep neural networks (DNNs) have mainly focused on innovations in
network architecture and loss function. In this paper, we introduce a flexible high-order …

Infrared remote sensing imaging has a wide range of military and civilian applications. The
detection of dim small targets is one of the most valuable research topics in this field …

Despite recent stereo matching networks achieving impressive performance given sufficient
training data, they suffer from domain shifts and generalize poorly to unseen domains. We …

Nowadays, video surveillance systems are commonly found in most public and private
spaces. These systems typically consist of a network of cameras that feed into a central …

Feature embeddings derived from continuous map** using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …