Recovering texture information from the aliasing regions has always been a major challenge for single image super-resolution (SISR) task. These are often submerged in noise so that we have to restore details while suppressing noise. To address this issue, propose an efficient Balanced Attention Mechanism (BAM), which consists of Avgpool Channel Module (ACAM) and Maxpool Spatial (MSAM) parallel....

Recent empirical works reveal that visual representation learned by deep neural networks can be successfully used as descriptors for image retrieval. A common technique is to leverage pre-trained models learn ranking losses and fine-tuning with labeled data. However, retrieval systems’ performance significantly decreases when querying images of lower resolution than the training images. This st...

The application of dynamic gestures is extensive in the field automated intelligent manufacturing. Due to temporal and spatial complexity gesture data, traditional machine learning algorithms struggle extract accurate features. Existing recognition have complex network designs, high parameter counts, inadequate feature extraction. In order solve problems low accuracy computational current recog...

Precisely segmenting the hippocampus from brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced segmentation algorithm based on 3D U-Net that can significantly increase performance. First, a dynamic convolution block designed to extract information more comprehensively in steps of U-Net’s encoder and de...

Mineral image classification technology based on machine vision is an efficient system for ore sorting. With the development of artificial intelligence and computer technology, deep learning-based mineral gradually applied to However, there a bottleneck in improving accuracy, feature extraction ability CNNs model relatively limited multi-category tasks. Therefore, four visual attention blocks a...

This article proposes a novel hierarchical residual network with attention mechanism (HResNetAM) for hyperspectral image (HSI) spectral-spatial classification to improve the performance of conventional deep learning networks. The straightforward convolutional neural network-based models have limitations in exploiting multiscale spatial and spectral features, this is key factor dealing high-dime...

The detection of small objects is easily affected by background information, and a lack context information makes difficult. Therefore, object has become an extremely challenging task. Based on the above problems, we proposed Single-Shot MultiBox Detector with attention mechanism dilated convolution (ADSSD). In module, strengthened connection between in space channels while using cross-layer co...

Abstract Face occlusion is a common problem in real life. Aiming at the that face detection effect poor when there natural environment, this paper proposes RetinaNet network based on fusion image enhancement and attention mechanism. Traditional RetinaNet, as typical single-stage detector, does not explicitly give process of extracting candidate regions directly obtains results object detection....