IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Despite the remarkable progress achieved in conventional instance segmentation, the problem of predicting instance segmentation results for unobserved future frames remains challenging due to the unobservability of future data. Existing methods mainl...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Among the greatest of the challenges of minimally invasive surgery (MIS) is the inadequate visualisation of the surgical field through keyhole incisions. Moreover, occlusions caused by instruments or bleeding can completely obfuscate anatomical landm...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait tem...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to ...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Architecture topology and architecture size have been regarded as two of th...
IEEE transactions on pattern analysis and machine intelligence
Jun 3, 2022
The convolutional neural network (CNN) has become a basic model for solving many computer vision problems. In recent years, a new class of CNNs, recurrent convolution neural network (RCNN), inspired by abundant recurrent connections in the visual sys...
Langmuir : the ACS journal of surfaces and colloids
Jun 3, 2022
In this study, a wettability-predicting method that uses an artificial neural network (ANN) by learning from digital images of the actual surface structures was developed. Polyester film surfaces were treated with oxygen plasma to realize various nan...
Sensors (Basel, Switzerland)
Jun 3, 2022
Performing the machining of complex surfaces can be a challenging task for a robot, especially in terms of collaborative robotics, where the available motion capabilities are greatly reduced in comparison with conventional industrial robot arms. It i...
BMC medical imaging
Jun 3, 2022
PURPOSE: To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children.