Spatial Channel Attention Research Articles

Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features with Channel-Spatial Attention

Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features with Channel-Spatial Attention

ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention

ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention

Research on a vehicle and pedestrian detection algorithm based on improved attention and feature fusion.

With the widespread integration of deep learning in intelligent transportation and various industrial sectors, target detection technology is gradually becoming one of the key research areas. Accurately detecting road vehicles and pedestrians is of great significance for the development of autonomous driving technology. Road object detection faces problems such as complex backgrounds, significant scale changes, and occlusion. To accurately identify traffic targets in complex environments, this paper proposes a road target detection algorithm based on the enhanced YOLOv5s. This algorithm introduces the weighted enhanced polarization self attention (WEPSA) self-attention mechanism, which uses spatial attention and channel attention to strengthen the important features extracted by the feature extraction network and suppress insignificant background information. In the neck network, we designed a weighted feature fusion network (CBiFPN) to enhance neck feature representation and enrich semantic information. This strategic feature fusion not only boosts the algorithm's adaptability to intricate scenes, but also contributes to its robust performance. Then, the bounding box regression loss function uses EIoU to accelerate model convergence and reduce losses. Finally, a large number of experiments have shown that the improved YOLOv5s algorithm achieves mAP@0.5 scores of 92.8% and 53.5% on the open-source datasets KITTI and Cityscapes. On the self-built dataset, the mAP@0.5 reaches 88.7%, which is 1.7%, 3.8%, and 3.3% higher than YOLOv5s, respectively, ensuring real-time performance while improving detection accuracy. In addition, compared to the latest YOLOv7 and YOLOv8, the improved YOLOv5 shows good overall performance on the open-source datasets.

GRAPE LEAF DISEASE RECOGNITION BASED ON A MULTISCALE MIXED ATTENTION RESIDUAL NETWORK

A multiscale efficient channel attention spatial-residual network (MECAS-ResNet) is proposed in this paper. On the basis of ResNet50, the training speed and model volume are reduced by group convolution. Through multiscale convolution, the spatial attention and effective channel attention are fused into the model to improve the network's attention toward various regions of lesions. A Wasserstein generative adversarial network is used for data enrichment, and the resulting images are diverse and variable. The enriched dataset is fed into the MECAS-ResNet model for identification and comparison to other models. After the improvement, the optimal recognition accuracy reached 95.31%, which is 10.02% higher than that on the original dataset; the model size is only 30.88 MB; and the average F1-score reached 95.30%. The improved model has higher recognition performance for similar diseases in different degrees, which is better than other models. The data generated by the experiment can be used for grape disease recognition, which greatly reduces the cost of data collection and provides technical support for grape disease recognition and precise drug treatment.

An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks.

Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanwhile, the accuracy is limited by the experience of the pathologist. Therefore, deep learning-empowered computer-aided diagnosis (CAD) is emerging as an important topic in digital pathology to streamline the standard process of automatic tissue analysis. Automated accurate nucleus segmentation can not only help pathologists make more accurate diagnosis, save time and labor, but also achieve consistent and efficient diagnosis results. However, nucleus segmentation is susceptible to staining variation, uneven nucleus intensity, background noises, and nucleus tissue differences in biopsy specimens. To solve these problems, we propose Deep Attention Integrated Networks (DAINets), which mainly built on self-attention based spatial attention module and channel attention module. In addition, we also introduce a feature fusion branch to fuse high-level representations with low-level features for multi-scale perception, and employ the mark-based watershed algorithm to refine the predicted segmentation maps. Furthermore, in the testing phase, we design Individual Color Normalization (ICN) to settle the dyeing variation problem in specimens. Quantitative evaluations on the multi-organ nucleus dataset indicate the priority of our automated nucleus segmentation framework.

Spatial–Channel Attention Transformer With Pseudo Regions for Remote Sensing Image-Text Retrieval

Spatial–Channel Attention Transformer With Pseudo Regions for Remote Sensing Image-Text Retrieval

CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction

CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction

Channel-Spatial Attention Guided CycleGAN for CBCT-Based Synthetic CT Generation to Enable Adaptive Radiotherapy

Channel-Spatial Attention Guided CycleGAN for CBCT-Based Synthetic CT Generation to Enable Adaptive Radiotherapy

ECW-EGNet: Exploring Cross-ModalWeighting and edge-guided decoder network for RGB-D salient object detection

Existing RGB-D salient object detection (SOD) techniques concentrate on combining data from multiple modalities (e.g., depth and RGB) and extracting multi-scale data for improved saliency reasoning. However, they frequently perform poorly as a factor of the drawbacks of low-quality depth maps and the lack of correlation between the extracted multi-scale data. In this paper, we propose a Exploring Cross-ModalWeighting and Edge-Guided Decoder Network (ECW-EGNet) for RGB-D SOD, which includes three prominent components. Firstly, we deploy a Cross-Modality Weighting Fusion (CMWF) module that utilizes Channel-Spatial Attention Feature Enhancement (CSAE) mechanism and Depth-Quality Assessment (DQA) mechanism to achieve the cross-modal feature interaction. The former parallels channel attention and spatial attention enhances the features of extracted RGB streams and depth streams while the latter assesses the depth-quality reduces the detrimental influence of the low-quality depth maps during the cross-modal fusion. Then, in order to effectively integrate multi-scale features for high-level and produce salient objects with precise locations, we construct a Bi-directional Scale- Correlation Convolution (BSCC) module in a bi-directional structure. Finally, we construct an Edge-Guided (EG) decoder that uses the edge detection operator to obtain edge masks to guide the enhancement of salient map edge details. The comprehensive experiments on five benchmark RGB-D SOD datasets demonstrate that the proposed ECW-EGNet outperforms 21 state-of-the-art (SOTA) saliency detectors in four widely used evaluation metrics.

ISCMIS:Spatial-Channel Attention Based Deep Invertible Network for Multi-Image Steganography

iSCMIS:Spatial-Channel Attention Based Deep Invertible Network for Multi-Image Steganography

Hybrid CNN-transformer based meta-learning approach for personalized image aesthetics assessment

Personalized Image Aesthetics Assessment (PIAA) is highly subjective, as people's aesthetic preferences vary greatly. Traditional generic models struggle to capture the unique preferences of each individual, and PIAA often deals with limited samples from individual users. Furthermore, it requires a holistic consideration of diverse visual features in images, including both local and global features. To address these challenges, we propose an innovative network that combines the power of transformer and Convolutional Neural Networks (CNNs) with Meta-Learning for PIAA (TCML-PIAA). Firstly, we leverage both Vision Transformer blocks and CNNs to extract long-term and short-term dependencies, mining richer and heterogeneous aesthetic attributes from these two branches. Secondly, to effectively fuse these distinct features, we introduce an Aesthetic Feature Interaction Module (AFIM), designed to seamlessly integrate the aesthetic features extracted from CNNs and ViT, enabling the interaction and fusion of aesthetic information across different modalities. We also incorporate a Channel-Spatial Attention Module (CSAM), embedding it within both the CNNs and the AFIM to enhance the perception of different regions in images, further exploring the aesthetic cues in images. Experimental results demonstrate that our TCML-PIAA outperforms existing state-of-the-art methods on benchmark databases.

Spectrum-based channel attention cooperating with time continuity encoding in transformer for EEG emotion analysis

Recently, electroencephalogram (EEG) emotion analysis has attracted increasing attention in many fields, such as neuroscience and brain-computer fusion. Due to the spatial channel difference and time continuity of emotional responses, it is necessary to capture relevant properties from EEG signals. In this paper, spectrum-based spatial channel attention is developed to reveal the emotional responses of different electrode channels located in different cerebral cortex regions. In addition, a time continuity encoding mechanism is developed to encode the temporal relations of time series signals in the transformer. In our model, the spectrum-based spatial channel attention cooperates with the time continuity encoding mechanism to fully utilize the spectral, spatial and temporal features of EEG signals (SST-Emo). Extensive experiments with different window sizes (1 s and 3 s) are carried out on DEAP for both binary and multiclass classification tasks. The accuracies are 96.28 %, 95.25 %, 95.52 %, 95.83 % and 93.88 % for 1 s window-size on arousal, valence, dominance, liking and 5-class, respectively. For 3 s window-size, the accuracies are 96.13 %, 95.74 %, 95.23 %, 95.7 % and 95.83 %, respectively. The results show that SST-Emo is superior to the state-of-the-art methods, which indicates that SST-Emo is able to extract more discriminative emotional representations.

Dual encoding DDS‐UNet liver tumour segmentation based on multi‐scale deep and shallow feature fusion

AbstractThe fusion and utilization of multi‐scale deep and shallow features are of great significance in liver tumour segmentation. This study proposes a dual encoding DDS‐UNet liver tumour segmentation method based on multi‐scale deep and shallow feature fusion, aiming to fully achieve the fusion and utilization of deep and shallow features and achieve accurate segmentation. The proposed method mainly consists of residual convolution module fusion residual convolution (FRC), dual encoding end fusion module dual encoding end fusion (DEF), and skip connection fusion module jump connection fusion module (JCF). In the residual convolution module, a layer by layer fused residual convolution is used instead of traditional convolution to achieve better training. In the dual encoding end fusion module, multi‐scale feature fusion at the end provides more comprehensive contextual information, solves the loss of spatial geometric information. The skip connection fusion module reduces the interference of invalid features by changing the weights of spatial attention and channel attention on important features. The Dice coefficient, average intersection to union ratio, accuracy, recall, and accuracy indicators tested on the LiTS dataset were 90.37%, 90.16%, 93.78%, 94.91%, and 98.84%, respectively, which are superior to many advanced liver tumor segmentation methods.

Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network

The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.

Forecasting top oil temperature for UHV reactor using Seq2Seq model with convolutional block attention mechanism

The top oil temperature in ultra-high voltage (UHV) reactors has attracted enormous interest due to its wide applications in fault diagnosis and insulation evaluation. In this work, the precise prediction method based on the Seq2Seq module with the convolutional block attention mechanism is proposed for the UHV reactor. To reduce the influence of vibratility and improve computational efficiency, a combination of the encoding layer and decoding layer named Seq2Seq is performed to reconstruct the complex raw data. The convolutional block attention mechanism (CBAM), composed of spatial attention and channel attention, is utilized to maximize the use of information in data. The Seq2Seq-CBAM is established to forecast the variation tendency of the oil temperatures in the UHV reactor. The experimental results show that the proposed method achieves high prediction accuracy for the top oil temperature in both single-step and multi-step.

A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid.

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model's detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.

A deep learning approach for object detection of rockfish in challenging underwater environments

IntroductionPreserving the marine ecological environment and safeguarding marine species is a global priority. However, human overfishing has led to a drastic decline in fish species with longer growth cycles, disrupting the equilibrium of the marine ecosystem. To address this issue, researchers are turning to deep learning techniques and state-of-the-art underwater devices, such as underwater robots, to explore the aquatic environment and monitor the activities of endangered populations. This approach has emerged as a focal point of recent research in protecting the marine ecological environment. This study employs a deep learning-based object detection algorithm to identify fish species in complex underwater environments.MethodsThe algorithm is built upon the You Only Look Once version 7(YOLOv7) algorithm, with the addition of the attention mechanism Convolutional Block Attention Module (CBAM) in the network’s backbone. CBAM enhances the feature maps through the fusion of spatial attention and channel attention, ultimately improving the robustness and accuracy of the model’s inference by replacing the original loss function CIoU with SCYLLAIntersection over Union(SIoU). In this paper, the rockfish pictures in the dataset Label Fishes in the Wild published by the National Marine Fisheries Service are selected, and the underwater image enhancement model (UWCNN) is introduced to process the pictures.ResultThe experimental results show that the mean average precision (mAP) value of the improved model on the test set is 94.4%, which is 3.5% higher than the original YOLOv7 model, and the precision and recall rate are 99.1% and 99%, respectively. The detection performance of the algorithm in the field of complex underwater environment is improved.DiscussionThe underwater fish detection scheme proposed in this study holds significant practical value and significance in promoting the conservation of marine ecosystems and the protection of fish species.

Learning multiple attention transformer super-resolution method for grape disease recognition

Grape leaf disease is a primary factor affecting grape yield and impeding the growth of the industrial economy. Currently, high-quality datasets are required for deep recognition models to effectively identify grape leaf diseases. In reality, the acquired image inevitably has the appearance of being low quality and unclear due to the influence of equipment and environment. The super-resolution method is an effective way to solve the problem. However, due to the complex model structure and learning strategies, existing super-resolution methods are computationally expensive and neglect the balance between global and local feature extraction. In this paper, a novel multiple attention transformer super-resolution (MATSR) method is proposed to solve these problems. Specifically, the method uses convolution to extract low-level features, which are then fed into the multiple attention shuffle transformer (MAST) module. The module consists of the shuffle transformer for global feature extraction, the constructed channel spatial attention block (CSAB) for local feature extraction, and the built reduisal feature block (RFB) for aggregating these two features. Meanwhile, considering the importance of the balance of global and local features for grape leaf disease recognition, a dynamic learning strategy is designed to balance these two features. Further, the abstract feature extraction layer combined the learned features with the low-level features. Finally, these abstracted features are fed into the pixels shuffle layer for super-resolution upsampling. Numerous comparative experiments are carried out on the collected high-resolution grape leaf disease images as well as the apple leaf pathological images. The results demonstrate the proposed method achieved the best PSNR of 38.152 dB and SSIM of 0.96 at scale ×2, and 35.896 dB and SSIM of 0.934 at scale ×3, respectively. Furthermore, four recognition models are experimented on the super-resolution grape disease dataset, among which ShuffleNet and Inception achieve the best accuracy of 98.28 % and 98.53 %, respectively. Therefore, this study can effectively aid grape growers in accurately identifying diseases and taking proactive measures.

ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception.

The Segmentation of infected areas from COVID-19 chest X-ray (CXR) images is of great significance for the diagnosis and treatment of patients. However, accurately and effectively segmenting infected areas of CXR images is still challenging due to the inherent ambiguity of CXR images and the cross-scale variations in infected regions. To address these issues, this article proposes a ERGPNet based on embedded residuals and global perception, to segment lesion regions in COVID-19 CXR images. First, aiming at the inherent fuzziness of CXR images, an embedded residual convolution structure is proposed to enhance the ability of internal feature extraction. Second, a global information perception module is constructed to guide the network in generating long-distance information flow, alleviating the interferences of cross-scale variations on the algorithm's discrimination ability. Finally, the network's sensitivity to target regions is improved, and the interference of noise information is suppressed through the utilization of parallel spatial and serial channel attention modules. The interactions between each module fully establish the mapping relationship between feature representation and information decision-making and improve the accuracy of lesion segmentation. Extensive experiments on three datasets of COVID-19 CXR images, and the results demonstrate that the proposed method outperforms other state-of-the-art segmentation methods of CXR images.

Random hand gesture authentication via efficient Temporal Segment Set Network

Biometric authentication technologies are rapidly gaining popularity, and hand gestures are emerging as a promising biometric trait due to their rich physiological and behavioral characteristics. Hand gesture authentication can be categorized as defined hand gesture authentication and random hand gesture authentication. Unlike defined hand gesture authentication, random hand gesture authentication is not constrained to specific hand gesture types, allowing users to perform hand gestures randomly during enrollment and verification, thus more flexible and friendly. However, in random hand gesture authentication, the model needs to extract more generalized physiological and behavioral features from different viewpoints and positions without gesture templates, which is more challenging. In this paper, we present a novel efficient Temporal-Segment-Set-Network (TS2N) that directly extracts both behavioral and physiological features from a single RGB video to further enhance the performance of random hand gesture authentication. Our method adopts a new motion pseudo-modality and leverages a set-based representation to capture behavioral characteristics online. Additionally, we propose a channel-spatial attention mechanism, Contextual Squeeze-and-Excitation Network (CoSEN), to better abstract and understand physiological characteristics by explicitly modeling the channel-spatial interdependence, thereby adaptively recalibrating channel-specific and spatial-specific responses. Extensive experiments on the largest public hand gesture authentication dataset SCUT-DHGA demonstrate TS2N’s superiority against 21 state-of-the-art models in terms of EER (5.707% for full version and 6.664% for lite version) and computational cost (98.9022G for full version and 46.3741G for lite version). The code is available at https://github.com/SCUT-BIP-Lab/TS2N.