Feature fusion graph attention network for link prediction

2021 ◽  
Author(s):  
Xuan Zhang ◽  
WangQun Chen ◽  
FuQiang Lin ◽  
XinYi Chen ◽  
Bo Liu
Keyword(s):  
Link Prediction ◽  
Feature Fusion ◽  
Attention Network

NHBS-Net: A Feature Fusion Attention Network for Ultrasound Neonatal Hip Bone Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Ruhan Liu ◽  
Mengyao Liua ◽  
Bin Sheng ◽  
Huating Li ◽  
Ping Li ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Bone Segmentation ◽  
Attention Network ◽  
Neonatal Hip

scholarly journals DTFA-Net: Dynamic and Texture Features Fusion Attention Network for Face Antispoofing

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xin Cheng ◽  
Hongfei Wang ◽  
Jingmei Zhou ◽  
Hui Chang ◽  
Xiangmo Zhao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Feature Fusion ◽  
Texture Features ◽  
Replay Attack ◽  
Attention Network ◽  
Detection Failure ◽  
The Face ◽  
Recognition Systems ◽  
Common Face

For face recognition systems, liveness detection can effectively avoid illegal fraud and improve the safety of face recognition systems. Common face attacks include photo printing and video replay attacks. This paper studied the differences between photos, videos, and real faces in static texture and motion information and proposed a living detection structure based on feature fusion and attention mechanism, Dynamic and Texture Fusion Attention Network (DTFA-Net). We proposed a dynamic information fusion structure of an interchannel attention block to fuse the magnitude and direction of optical flow to extract facial motion features. In addition, for the face detection failure of HOG algorithm under complex illumination, we proposed an improved Gamma image preprocessing algorithm, which effectively improved the face detection ability. We conducted experiments on the CASIA-MFSD and Replay Attack Databases. According to experiments, the DTFA-Net proposed in this paper achieved 6.9% EER on CASIA and 2.2% HTER on Replay Attack that was comparable to other methods.

scholarly journals A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1271
Author(s):  
Hongmin Gao ◽  
Yiyan Zhang ◽  
Yunfei Zhang ◽  
Zhonghao Chen ◽  
Chenming Li ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Receptive Fields ◽  
Classification Performance ◽  
Redundant Information ◽  
Hyperspectral Image Classification ◽  
Training Process ◽  
Attention Network ◽  
Spatial Features

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

Attention-Based Multimodal Neural Network for Automatic Evaluation of Press Conferences

2020 ◽  
Vol 11 (3) ◽  
pp. 1-19
Author(s):  
Shengzhou Yi ◽  
Koshiro Mochitomi ◽  
Isao Suzuki ◽  
Xueting Wang ◽  
Toshihiko Yamasaki
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Language Model ◽  
Evaluation Criteria ◽  
Attention Network ◽  
Audio Features ◽  
Average Accuracy ◽  
Word Representation ◽  
Audio Data ◽  
Text Features

In the study, a multimodal neural network is proposed to automatically predict the evaluation of a professional consultant team for press conferences using text and audio data. Seven publicly available press conference videos were collected, and all the Q&A pairs between speakers and journalists were annotated by the consultant team. The proposed multimodal neural network consists of a language model, an audio model, and a feature fusion network. The word representation is made up by a token embedding using ELMo and a type embedding. The language model is an LSTM with an attention layer. The audio model is based on a six-layer CNN to extract segmental feature as well as an attention network to measure the importance of each segment. Two approaches of feature fusion are proposed: a shared attention network and the production of text features and audio features. The former can explain the importance between speech content and speaking style. The latter achieved the best performance with the average accuracy of 60.1% for all evaluation criteria.

Multi-Scale Attention Network Based on Multi-Feature Fusion for Person Re-Identification

2021 ◽  
Author(s):  
Minghao Li ◽  
Liming Yuan ◽  
Xianbin Wen ◽  
Jianchen Wang ◽  
Gengsheng Xie ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Attention Network ◽  
Multi Scale

scholarly journals Subtask Attention Based Object Detection in Remote Sensing Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1925
Author(s):  
Shengzhou Xiong ◽  
Yihua Tan ◽  
Yansheng Li ◽  
Cai Wen ◽  
Pei Yan
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Feature Fusion ◽  
Detection Task ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Attention Network ◽  
Multi Scale ◽  
Automatic Interpretation

Object detection in remote sensing images (RSIs) is one of the basic tasks in the field of remote sensing image automatic interpretation. In recent years, the deep object detection frameworks of natural scene images (NSIs) have been introduced into object detection on RSIs, and the detection performance has improved significantly because of the powerful feature representation. However, there are still many challenges concerning the particularities of remote sensing objects. One of the main challenges is the missed detection of small objects which have less than five percent of the pixels of the big objects. Generally, the existing algorithms choose to deal with this problem by multi-scale feature fusion based on a feature pyramid. However, the benefits of this strategy are limited, considering that the location of small objects in the feature map will disappear when the detection task is processed at the end of the network. In this study, we propose a subtask attention network (StAN), which handles the detection task directly on the shallow layer of the network. First, StAN contains one shared feature branch and two subtask attention branches of a semantic auxiliary subtask and a detection subtask based on the multi-task attention network (MTAN). Second, the detection branch uses only low-level features considering small objects. Third, the attention map guidance mechanism is put forward to optimize the network for keeping the identification ability. Fourth, the multi-dimensional sampling module (MdS), global multi-view channel weights (GMulW) and target-guided pixel attention (TPA) are designed for further improvement of the detection accuracy in complex scenes. The experimental results on the NWPU VHR-10 dataset and DOTA dataset demonstrated that the proposed algorithm achieved the SOTA performance, and the missed detection of small objects decreased. On the other hand, ablation experiments also proved the effects of MdS, GMulW and TPA.

scholarly journals Object Tracking in RGB-T Videos Using Modal-Aware Attention Network and Competitive Learning

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 393
Author(s):  
Hui Zhang ◽  
Lei Zhang ◽  
Li Zhuo ◽  
Jing Zhang
Keyword(s):  
Feature Extraction ◽  
Object Tracking ◽  
Feature Fusion ◽  
Imaging System ◽  
Rapid Development ◽  
Competitive Learning ◽  
Superior Performance ◽  
Stream Network ◽  
Attention Network ◽  
Dual Modality

Object tracking in RGB-thermal (RGB-T) videos is increasingly used in many fields due to the all-weather and all-day working capability of the dual-modality imaging system, as well as the rapid development of low-cost and miniaturized infrared camera technology. However, it is still very challenging to effectively fuse dual-modality information to build a robust RGB-T tracker. In this paper, an RGB-T object tracking algorithm based on a modal-aware attention network and competitive learning (MaCNet) is proposed, which includes a feature extraction network, modal-aware attention network, and classification network. The feature extraction network adopts the form of a two-stream network to extract features from each modality image. The modal-aware attention network integrates the original data, establishes an attention model that characterizes the importance of different feature layers, and then guides the feature fusion to enhance the information interaction between modalities. The classification network constructs a modality-egoistic loss function through three parallel binary classifiers acting on the RGB branch, the thermal infrared branch, and the fusion branch, respectively. Guided by the training strategy of competitive learning, the entire network is fine-tuned in the direction of the optimal fusion of the dual modalities. Extensive experiments on several publicly available RGB-T datasets show that our tracker has superior performance compared to other latest RGB-T and RGB tracking approaches.

scholarly journals PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection

2020 ◽  
Vol 16 (12) ◽  
pp. 7448-7458 ◽  
Author(s):  
Hongwen Dong ◽  
Kechen Song ◽  
Yu He ◽  
Jing Xu ◽  
Yunhui Yan ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Feature Fusion ◽  
Attention Network ◽  
Global Context ◽  
Surface Defect Detection
Sign in / Sign up

Export Citation Format

Share Document