Results 71 to 80 of about 4,140 (257)
Semi-supervised Learning Using Siamese Networks [PDF]
, 2019 added link of GitHub ...
Attaullah Sahito +2 more
openaire +2 more sources
3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends
Advanced Robotics Research, EarlyView.Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu +5 more
wiley +1 more source
Masked Siamese Networks for Label-Efficient Learning
, 2022 We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our approach matches the representation of an image view containing randomly masked patches to the representation of the original unmasked image.
Mahmoud Assran +8 more
openaire +2 more sources
Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots
Advanced Robotics Research, EarlyView.This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou +4 more
wiley +1 more source
Semantic and context features integration for robust object tracking
IET Image Processing, 2022 Siamese network‐based object tracking learns features of a target object marked in the first frame and that of the object in subsequent frames simultaneously and then measures similarity between two features to recognize and locate the object.
Jinzhen Yao +3 more
doaj +1 more source
Interactive Instance Annotation with Siamese Networks
CoRRAnnotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. However, most existing methods focus on in-domain scenarios, limiting their effectiveness for cross-domain annotation tasks.
Xiang Xu +5 more
openaire +2 more sources
Partial Discharge Diagnosis With Siamese Fusion Network
IEEE Access, 2022 Partial discharge is a common fault type in the operation of power equipment. Recently, deep learning methods have shown great potential in partial discharge (PD) diagnosis. These methods construct a fitting relationship between input and output with mass training samples.
Zhihong Huang +3 more
openaire +2 more sources
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
EURASIP Journal on Image and Video Processing, 2019 With the popular use of high-resolution satellite images, remote sensing scene classification has always been a hot research topic in its related areas. However, limited to the issues of remote sensing datasets including the small scale of scene classes,
Yong Zhou +6 more
doaj +1 more source
Deep Contrastive Learning for High‐Throughput Prediction of Drug Resistance Mutations from Sequences
Advanced Science, EarlyView.This study presents DeepMutDTA, a deep learning framework aimed at predicting mutation‐induced changes in protein‐drug interactions and prioritizing variants potentially linked to drug resistance. Trained on large‐scale data, it incorporates SimSiam‐MuTF, a label‐aware contrastive fine‐tuning strategy that encourages separation between WT and MT ...
Xiaowen Hu +7 more
wiley +1 more source