Spatial-Channel Transformer for Scene Recognition

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver’s distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver’s behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

Object tracking is one of the important research topics in the direction of computer vision. And occlusion, deformation and scale change are the most challenging issues in the realm of tracking. In an effort to address the issue that the Siamese network-based object tracking algorithm’s resilience and success rate are both low and weak in complicated circumstances such as similar object interference and object occlusion, a Siamese network Object tracking algorithm combined with attention mechanism based on the existing Siamese network algorithm is proposed. Firstly, in order to extract the features of the template picture and the sample picture, the modified AlexNet convolutional neural network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> is utilized as the feature extraction network. Secondly, in order to increase the model’s precision and increase tracking’s rate of success, the module for the attention mechanism, which consists of the channel attention mechanism and the spatial attention mechanism, is introduced. The channel attention mechanism can focus on the information useful for tracking the Object, suppress unimportant background information, and the spatial attention mechanism is capable of picking up on the object’s abundant spatial information. Experiments show that the proposed Siamese network target tracking algorithm combined with attention mechanism achieves contestable results on the OTB100 test dataset, and the enhanced method outperforms the original approach in terms of performance.

With the development and progress of medical imaging technology, the resolution of medical images has been increasing, and a variety of high-definition imaging modalities such as CT, PET-CT and MRI have emerged. U-Net network has the advantages of simple network topology and small training set data requirement; thus, the field of medical image segmentation uses it extensively. However, U-Net also has some problems, such as edge loss of segmentation results, long training time and single application scenario. For the medical image segmentation problem, this paper proposes a method that combines channel attention and spatial attention and uses an improved join strategy to join the network structure. To address the problem of insufficient data volume of medical images, this paper performs a data augmentation operation on the dataset with elastic deformation. In addition, we use a local-global training strategy to further improve the performance of training on medical images. When compared to the original U-Net, the Dice coefficient and IOU metrics are significantly better when utilizing the method suggested in this work. After extensive experiments, the strategy proposed in this study can achieve good outcomes when facing medical image segmentation problems and has great potential.

This paper presents a dual-structured convolutional neural network (CNN) for image classification, which integrates two parallel branches: CNN-A with spatial attention and CNN-B with channel attention. The spatial attention module in CNN-A dynamically emphasizes discriminative regions by aggregating channel-wise information, while the channel attention mechanism in CNN-B adaptively recalibrates feature channel importance. The extracted features from both branches are fused through concatenation, enhancing the model’s representational capacity by capturing complementary spatial and channel-wise dependencies. Extensive experiments on a 12-class image dataset demonstrate the superiority of the proposed model over state-of-the-art methods, achieving 98.06% accuracy, 96.00% precision, and 98.01% F1-score. Despite a marginally longer training time, the model exhibits robust convergence and generalization, as evidenced by stable loss curves and high per-class recognition rates (&gt;90%). The results validate the efficacy of dual attention mechanisms in improving feature discrimination for complex image classification tasks.

Detecting dangerous goods in security images is a challenging task. To overcome the challenges of localization difficulty and directional feature loss of contraband in X-ray images, our proposed solution, R3Det, employs the Convolutional Block Attention Module (CBAM). By integrating ResNeSt into the original detector, our detector includes a soft attention mechanism to redistribute weights among feature channels. This enhances the network’s ability to extract important features and facilitates extraction of target objects features under complex backgrounds. Subsequently, we introduced the spatial and channel attention mechanism during the connection between the backbone and the Feature Pyramid Network (FPN), enabling the model to focus on significant features while ignoring complex background information, then the following Feature Refinement Module to achieve feature alignment in a pixel-by-pixel manner. Our approach successfully achieved rotating target detection in the background of complex X-ray images. Through end-to-end training, our proposed method achieves a 2.6% improvement over the original detector, with a mean Average Precision (mAP) of 86.7%. Notably, our approach showed remarkable results in detecting sensors, pressure, and firetrackers. Now, we have deployed our proposed method on actual security machines for hazardous material detection tasks.

Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

Older People Issues and Innovations in Care 3rd edition

Track-by-attention model, such as TrackFormer, have been one of the most popular deep models for multi-object tracking (MOT). However, one of their core limitations is image recognition accuracy and local feature attention due to the long sequence attention mechanism. To remedy this problem, MEFormer, a multi-scale feature enhanced model is proposed, in which both the channel attention and spatial attention mechanisms are leveraged for feature enhanced. MEFormer can effectively capture discriminative representation for MOT with attention and aggregation. Experiments on the MOT17 dataset indicate that the proposed MEFormer is able to achieve competitive performance on the task of multi-object tracking with 56.0% MOTA and 2400 ID switches.

In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance.

Automated actual evapotranspiration estimation: Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

The heart is one of the most important organs of humans. Accurate cardiac segmentation can improve the efficiency of cardiac diagnosis, and thus boost the clinical applications in the future. Although deep learning has enormous success in recent years, medical cardiac segmentation is still an unsolved problem due to the unique characteristics of medical images obtained by computerized tomography or magnetic resonance imaging. In this paper, we propose a new cardiac segmentation method for CT images. Our key idea is to introduce attention mechanisms, both in the spatial domain and channels, to improve the recent UNet++ network structure for medical images. The spatial and channel attention mechanisms can guide the network to focus on important information, which is effective for medical images, thereby improving segmentation details and accuracy. We evaluated our method on our own four-chamber heart dataset, and experiments demonstrate that the proposed method can obtain encouraging segmentation performance, and higher accuracy results compared with other methods.

Volleyball training video classification description using the BiLSTM fusion attention mechanism

Image captioning refers to the process of generating a textual description that describes objects and activities present in a given image. It connects two fields of artificial intelligence, computer vision, and natural language processing. Computer vision and natural language processing deal with image understanding and language modeling, respectively. In the existing literature, most of the works have been carried out for image captioning in the English language. This article presents a novel method for image captioning in the Hindi language using encoder–decoder based deep learning architecture with efficient channel attention. The key contribution of this work is the deployment of an efficient channel attention mechanism with bahdanau attention and a gated recurrent unit for developing an image captioning model in the Hindi language. Color images usually consist of three channels, namely red, green, and blue. The channel attention mechanism focuses on an image’s important channel while performing the convolution, which is basically to assign higher importance to specific channels over others. The channel attention mechanism has been shown to have great potential for improving the efficiency of deep convolution neural networks (CNNs). The proposed encoder–decoder architecture utilizes the recently introduced ECA-NET CNN to integrate the channel attention mechanism. Hindi is the fourth most spoken language globally, widely spoken in India and South Asia; it is India’s official language. By translating the well-known MSCOCO dataset from English to Hindi, a dataset for image captioning in Hindi is manually created. The efficiency of the proposed method is compared with other baselines in terms of Bilingual Evaluation Understudy (BLEU) scores, and the results obtained illustrate that the method proposed outperforms other baselines. The proposed method has attained improvements of 0.59%, 2.51%, 4.38%, and 3.30% in terms of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores, respectively, with respect to the state-of-the-art. Qualities of the generated captions are further assessed manually in terms of adequacy and fluency to illustrate the proposed method’s efficacy.

Owing to its important role in video surveillance, person re-identification(re-ID) has become a hot research field of computer vision. The purpose of person re-ID is to identify the same person under different cameras. Domain adaptive person re-ID is to transfer the training model from labeled dataset to unlabeled dataset. Due to the neglect of key channels and key regions, the performance of most existing domain adaptive person re-ID methods is limited. To solve the above problem, this paper proposed a mutual attention learning network model (MALNet). It is based on channel attention and spatial attention mechanism. It consists of two channel-spatial attention convolutional neural networks (channel-spatial attention CNNs). Two networks are pre-trained in a source domain. They adapt to the target domain by the mutual attention learning strategy afterward. They distill and supervise each other to enhance the complementarity of MAL-Net. Moreover, a channel-spatial attention loss (CSAL) function is proposed to constrain and approximate the classification prediction distributions and feature triplet distributions. Extensive comparative experiments are carried out on two large-scale re-ID datasets (Market-1501 and DukeMTMC-reID). The performance of MAL-Net is better than other state-of-the-art methods, which proves the superiority and effectiveness of our method.