Results 91 to 100 of about 1,683,190 (291)

Deep learning for mango leaf disease identification: A vision transformer perspective

Heliyon
Over the last decade, the use of machine learning in smart agriculture has surged in popularity. Deep learning, particularly Convolutional Neural Networks (CNNs), has been useful in identifying diseases in plants at an early stage.
Md. Arban Hossain, Saadman Sakib, Hasan Muhammad Abdullah, Shifat E. Arman   +3 more
doaj   +1 more source

Neural Architecture Search for Transformers: A Survey

IEEE Access, 2022
Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) and Computer Vision (CV) domains.
Krishna Teja Chitty-Venkata, Murali Emani, Venkatram Vishwanath, Arun K. Somani   +3 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Waterline Extraction for Artificial Coast With Vision Transformers

Frontiers in Environmental Science, 2022
Accurate acquisition for the positions of the waterlines plays a critical role in coastline extraction. However, waterline extraction from high-resolution images is a very challenging task because it is easily influenced by the complex background.
Le Yang, Xing Wang, Jingsheng Zhai
doaj   +1 more source

Vision Language Transformers: A Survey

CoRR, 2023
Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling.
Clayton Fields, Casey Kennington
openaire   +2 more sources

Liquid Phase Transmission Electron Microscopy: A Window into the Early Stages of Complex Material Formation

Advanced Functional Materials, EarlyView.
Liquid‐phase transmission electron microscopy enables direct observation of nucleation and growth processes in solution. This review is dedicated to the remembrance of Helmut Cölfen and highlights recent studies on complex materials—oxides, biominerals, organic–inorganic crystals—which were central to his research activity. It summarizes key milestones,
Charles Sidhoum, Vinavadini Ramnarain, Nathaly Ortiz Peña, Dris Ihiawakrim, Clément Sanchez, Ovidiu Ersen   +5 more
wiley   +1 more source

Solution‐Processed Two‐Dimensional Indium Oxide on Sodium‐Embedded Alumina for Reconfigurable Optoelectronic Synaptic Transistors

Advanced Functional Materials, EarlyView.
Wafer‐scale two‐dimensioanl In2Se3 oxidized into InOx on sodium‐embedded beta‐alumina enables multifunctional reconfigurable electronics. Sodium ions accumulate within distinct spatial distribution under drain‐controlle and gate‐controlled operation. Drain‐control operation gives controllability of ultraviolet‐driven optoelectronic synaptic conductance
Jinhong Min, Yonghyun Albert Kwon, Hyunbin Kim, Jihyun Kim, Myeongjin Jung, Jaewon Heo, Gwang Ya Kim, Vlastimil Mazanek, Zdeněk Sofer, In Soo Kim, Jeong Ho Cho, Hyesung Park, Sang Min Won, Joohoon Kang   +13 more
wiley   +1 more source

Interpretability-Aware Vision Transformer

CoRR, 2023
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance. While there has been a surge of interest in developing {\it post hoc} solutions to explain ViTs' outputs, these methods do not generalize to different downstream tasks and ...
Yao Qiang, Chengyin Li, Prashant Khanduri, Dongxiao Zhu   +3 more
openaire   +2 more sources

Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition

Applied Sciences, 2023
Sign languages are complex, but there are ongoing research efforts in engineering and data science to recognize, understand, and utilize them in real-time applications.
Nojood M. Alharthi, Salha M. Alzahrani
doaj   +1 more source

Multimodal Perception and Machine Learning‐Empowered Human Machine Interfaces With Double‐Network Hydrogel Fibers

Advanced Functional Materials, EarlyView.
This work develops polyacrylamide‐alginate (PAM‐Alg) double‐network hydrogel fibers for multimodal perception and intelligent human‐machine interfaces. The covalent‐ionic network provides high strength, toughness, and stable conductivity. Easily woven into wearables and integrated with soft robots, the fibers enable object and temperature recognitions ...
Yujue Yang, Minyu Qiu, Di Tan, Xinlong Liu, Junze Zhang, Ziyu He, Jing Han, Yuanyuan Gao, Zihua Li, Hong Fu, Bingang Xu   +10 more
wiley   +1 more source