Research On Strategy Selection And Optimization Of Attention Mechanism In Computer Visio

Attention mechanism is an extremely common and important approach in the field of computer vision.It mimics the attention mechanism of the human visual system,allowing for selective focus on regions of interest.This technique improves the efficiency and accuracy of processing image or video data.Attention mechanism is a widely used and significant approach in the field of computer vision.It enhances the efficiency and accuracy of processing image or video data by selectively focusing on specific regions of interest.However,it is important to note that a single type of attention mechanism may not be suitable for all computer vision tasks,as different tasks have varied requirements.In addition,the inclusion of attention mechanisms increases the complexity of the model,which can create challenges in training and deploying the model.To address the aforementioned issues with attention mechanism neural networks,this paper introduces two novel attention mechanism modules,along with a neural network that incorporates these new attention modules and a dynamic pruning algorithm based on the attention mechanism.The first attention mechanism module is the multi-scale Large Receptive Field Attention(LRFA)module.It groups input feature information by channels,applies different numbers of convolutional kernels within each group to simulate various receptive field sizes,and adjusts kernel sizes based on the input feature map.This ensures that the maximum receptive field captures global feature information effectively.The module generates an attention map using the extracted features and applies weights to the input feature map to improve accuracy.Experimental results demonstrate that the proposed attention module outperforms advanced convolutional neural networks and selfattentive networks in tasks such as image classification and object detection in the field of computer vision.The second attention mechanism module introduced in this work is the Dynamic Aggregation Branch Attention(DABA)module.It consists of two branches: a multiscale convolutional branch and a self-attentive branch.Unlike simple superposition or concatenation methods,each branch is assigned a weight that is dynamically adjusted during the training process to adapt autonomously to various computer vision tasks.In the aggregation process,the weights assigned to each branch enable the module to effectively incorporate the strengths of both branches.This adaptive weighting mechanism allows the attention module to enhance its performance in image classification tasks.Experimental results demonstrate that the proposed attention module surpasses state-of-the-art convolutional neural networks and self-attentive networks in terms of accuracy and performance.Finally,in order to tackle the issue of increased model complexity,this paper introduces a dynamic pruning algorithm based on the attention mechanism.This algorithm incorporates the pruning operation into the training process and dynamically prunes channels in the neural network model based on attention weights and Frobenius parametrization.By removing redundant channels,the algorithm aims to reduce model complexity.Experimental results demonstrate that the proposed pruning algorithm is capable of achieving a pruning rate of 30% or more while maintaining negligible loss of accuracy.This indicates the effectiveness of the algorithm in reducing model complexity without significantly compromising performance.In this paper,the aforementioned attention module and pruning algorithm are implemented using the Py Torch deep learning framework.The performance of the proposed neural network is evaluated on popular datasets for various visual downstream tasks.Experimental results demonstrate that the proposed approach outperforms advanced attention mechanism neural networks and classical pruning algorithms,showcasing improved performance.