Results 111 to 120 of about 102,874 (306)
Transform Domain Learning for Image Recognition
IEEE AccessImage and video classification are distinct tasks in computer vision. Three-dimensional convolutional neural networks (3D CNNs) are commonly employed for video classification, while two-dimensional convolutional neural networks (2D CNNs) are more ...
Dengtai Tan, Jinlong Zhao, Shichao Li
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture ...
Xin Lu 0002 +4 more
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Parameter list of the three CNNs.
, 2019 Parameter list of the three CNNs.
Di Xue (696873) +4 more
core +1 more source
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
CoRR, 2018 There is a plenty of research going on in field of object recognition, but object state recognition has not been addressed as much. There are many important applications which can utilize object state recognition, such as, in robotics, to decide for how to grab an object.
openaire +2 more sources
SIRENA: A simulation environment for CNNs
, 1994 SIRENA is a general simulation environment for artificial neural networks, with emphasis towards CNNs. A special interest has been placed in allowing the simulation and modelling of the non-ideal effects expected from VLSI implementations.
Domínguez Castro, Rafael +5 more
core +1 more source
Longitudinal Morphomolecular Monitoring of Head and Neck Carcinogenesis
Advanced Science, EarlyView.A miniaturized endoscopic probe that integrates Raman spectroscopy and optical coherence tomography, enabling simultaneous molecular and structural imaging of living tissue, was developed. Applied longitudinally in a mouse model of head and neck cancer, the system tracks disease progression from precancerous lesions to invasive cancer.
Jianrong Qiu +7 more
wiley +1 more source
On global exponential stability of standard and Full-Range CNNs
, 2008 This paper compares the dynamical behaviour of the standard (S) cellular neural networks (CNNs) and the full-range (FR) CNNs, when the two CNN models are characterized by the same set of parameters (interconnections and inputs).
Pancioni, Luca +7 more
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Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials
Advanced Science, EarlyView.Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan +8 more
wiley +1 more source
Partial Large Kernel CNNs for Efficient Super-Resolution
IEEE AccessRecently, in the image super-resolution (SR) domain, Transformers have outperformed Convolution Neural Networks (CNNs) with reduced computational complexity and parameters by modeling long-range dependencies input-dependently.
Dongheon Lee, Seokju Yun, Youngmin Ro
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