Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been widely
employed in medical image segmentation. While CNNs excel in local feature encoding, their …

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for
robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (…

The optical detection methodology stands as a predominant approach for detecting
underwater bubbles. Nonetheless, owing to poor underwater imaging conditions, the acquired …

In semi-supervised medical image segmentation, the scarcity of labeled data makes models
prone to learning bias, causing persistent errors in certain regions and eventual over-fitting, …

With the popularity of monocular videos generated by video sharing and live broadcasting
applications, reconstructing and editing dynamic scenes in stationary monocular cameras has …

Occlusions hinder point cloud frame alignment in LiDAR data, a challenge inadequately
addressed by scene flow models tested mainly on occlusion-free datasets. Attempts to integrate …

In the domain of supervised scene flow estimation, the process of manual labeling is both
time-intensive and financially demanding. This paper introduces SSFlowNet, a semi-…

Semi-supervised learning is becoming increasingly popular in medical image segmentation
because of its ability to exploit large amounts of unlabelled data to extract additional …

The increasing development of information technology has led to a higher demand for
communication capacity. Moreover, the mode division multiplexing (MDM) is considered one of …

Chaos exhibits significant characteristics, including pseudo-randomness and unpredictability.
These properties render chaos a valuable tool in practical applications such as secure …