Active visual computing model based on data- and knowledge-driven selective attention mechanism

1999 ◽  
Author(s):  
Fuhui Long ◽  
Nanning Zheng ◽  
David D. Feng
Keyword(s):  
Selective Attention ◽  
Attention Mechanism ◽  
Computing Model ◽  
Model Based ◽  
Visual Computing

scholarly journals A Computing Model of Selective Attention for Service Robot Based on Spatial Data Fusion

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Huanzhao Chen ◽  
Guohui Tian
Keyword(s):  
Visual Attention ◽  
Selective Attention ◽  
Spatial Data ◽  
Attention Mechanism ◽  
Service Robots ◽  
Service Robot ◽  
Domestic Environment ◽  
Computing Model ◽  
Attention Selection ◽  
Visual Attention Mechanism

Robots and humans are facing the same problem: they all need to face a lot of perceptual information and choose valuable information. Before the robots provide services, they need to complete a robust real-time selective attention process in the domestic environment. Visual attention mechanism is an important part of human perception, which enables humans to select the visual focus on the most potential interesting information. It also could dominate the allocation of computing resource. It also could focus human’s attention on valuable objects in the home environment. Therefore we are trying to transfer visual attention selection mechanism to the scene analysis of service robots. This will greatly improve the robot’s efficiency in perception and processing information. We proposed a computing model of selective attention which is biologically inspired by visual attention mechanism, which aims at predicting focus of attention (FOA) in a domestic environment. Both static features and dynamic features are composed in attention selection computing process. Information from sensor networks is transformed and incorporated into the model. FOA is selected based on a winner-take-all (WTA) network and rotated by inhibition of return (IOR) principle. The experimental results showed that this approach is robust to the partial occlusions, scale-change illumination, and variations. The result demonstrates the effectiveness of this approach with available literature on biological evidence. Some specific domestic service tasks are also tailored to this model.

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

scholarly journals Person Reidentification Model Based on Multiattention Modules and Multiscale Residuals

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yongyi Li ◽  
Shiqi Wang ◽  
Shuang Dong ◽  
Xueling Lv ◽  
Changzhi Lv ◽  
...  
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Original Network ◽  
Fine Grained ◽  
Backbone Network ◽  
Model Based ◽  
Local Branch ◽  
Feature Expression ◽  
Global And Local

At present, person reidentification based on attention mechanism has attracted many scholars’ interests. Although attention module can improve the representation ability and reidentification accuracy of Re-ID model to a certain extent, it depends on the coupling of attention module and original network. In this paper, a person reidentification model that combines multiple attentions and multiscale residuals is proposed. The model introduces combined attention fusion module and multiscale residual fusion module in the backbone network ResNet 50 to enhance the feature flow between residual blocks and better fuse multiscale features. Furthermore, a global branch and a local branch are designed and applied to enhance the channel aggregation and position perception ability of the network by utilizing the dual ensemble attention module, as along as the fine-grained feature expression is obtained by using multiproportion block and reorganization. Thus, the global and local features are enhanced. The experimental results on Market-1501 dataset and DukeMTMC-reID dataset show that the indexes of the presented model, especially Rank-1 accuracy, reach 96.20% and 89.59%, respectively, which can be considered as a progress in Re-ID.

scholarly journals Soft-Computing Model-Based Controllers for Increased Photovoltaic Plant Efficiencies

2007 ◽  
Vol 22 (4) ◽  
pp. 873-880 ◽  
Author(s):  
Abdulhadi Varnham ◽  
Abdulrahman M. Al-Ibrahim ◽  
Gurvinder S. Virk ◽  
Djamel Azzi
Keyword(s):  
Soft Computing ◽  
Computing Model ◽  
Model Based ◽  
Photovoltaic Plant
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