Results 21 to 30 of about 52,284 (313)

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

IET Image Processing, 2023
Although most state‐of‐the‐art action recognition models have adopted a two‐stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models.
Zhaoqilin Yang, Gaoyun An, Ruichen Zhang, Zhenxing Zheng, Qiuqi Ruan   +4 more
doaj   +1 more source

Ph-Net: Parallelepiped Microstructure Homogenization Via 3d Convolutional Neural Networks

SSRN Electronic Journal, 2022
Microstructures are attracting academic and industrial interests with the rapid development of additive manufacturing. The numerical homogenization method has been well studied for analyzing mechanical behaviors of microstructures; however, it is too time-consuming to be applied to online computing or applications requiring high-frequency calling, e.g.,
Hao Peng, An Liu, Jingcheng Huang, Lingxin Cao, Jikai Liu, Lin Lu   +5 more
openaire   +2 more sources

LiteDEKR: End‐to‐end lite 2D human pose estimation network

IET Image Processing, 2023
The 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and ...
Xueqiang Lv, Wei Hao, Lianghai Tian, Jing Han, Yuzhong Chen, Zangtai Cai   +5 more
doaj   +1 more source

SA-Net: Shuffle Attention for Deep Convolutional Neural Networks [PDF]

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Attention mechanisms, which enable a neural network to accurately focus on all the relevant elements of the input, have become an essential component to improve the performance of deep neural networks. There are mainly two attention mechanisms widely used in computer vision studies, spatial attention and channel attention, which aim to capture the ...
Qing-Long Zhang, Yu-Bin Yang
openaire   +3 more sources

Deep Net Tree Structure for Balance of Capacity and Approximation Ability

Frontiers in Applied Mathematics and Statistics, 2019
Deep learning has been successfully used in various applications including image classification, natural language processing and game theory. The heart of deep learning is to adopt deep neural networks (deep nets for short) with certain structures to ...
Charles K. Chui, Charles K. Chui, Shao-Bo Lin, Shao-Bo Lin, Ding-Xuan Zhou   +4 more
doaj   +1 more source

Multi‐objective single‐shot neural architecture search via efficient convolutional filters

Electronics Letters, 2023
This paper presents a novel approach for fast neural architecture search (NAS) in Convolutional Neural Networks (CNNs) for end‐to‐end License Plate Recognition (LPR).
Seyed Mahdi Shariatzadeh, Mahmood Fathy, Reza Berangi   +2 more
doaj   +1 more source

Simplicial 2-Complex Convolutional Neural Nets

, 2020
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground,
Bunch, Eric, You, Qian, Fung, Glenn, Singh, Vikas   +3 more
openaire   +2 more sources

PANDA: Pose Aligned Networks for Deep Attribute Modeling [PDF]

, 2014
We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion.
Bourdev, Lubomir, Darrell, Trevor, Paluri, Manohar, Ranzato, Marc'Aurelio, Zhang, Ning   +4 more
core   +1 more source

BA-Net: Bridge Attention for Deep Convolutional Neural Networks

, 2022
In recent years, channel attention mechanism has been widely investigated due to its great potential in improving the performance of deep convolutional neural networks (CNNs) in many vision tasks. However, in most of the existing methods, only the output of the adjacent convolution layer is fed into the attention layer for calculating the channel ...
Yue Zhao, Junzhou Chen, Zirui Zhang, Ronghui Zhang   +3 more
openaire   +2 more sources

Vehicle Re-identification method based on Swin-Transformer network

Array, 2022
Vehicle Re-identification is to find out the exact vehicle captured by other cameras given a vehicle image. In natural traffic surveillance systems, vehicle re-identification can play a role in target vehicles localization, supervision, and criminal ...
Jianrong Li, Chang Yu, Jinyuan Shi, Chuanlei Zhang, Ting Ke   +4 more
doaj   +1 more source