Channel Dimensions Research Articles

BCMFIFuse: A Bilateral Cross-Modal Feature Interaction-Based Network for Infrared and Visible Image Fusion

The main purpose of infrared and visible image fusion is to produce a fusion image that incorporates less redundant information while incorporating more complementary information, thereby facilitating subsequent high-level visual tasks. However, obtaining complementary information from different modalities of images is a challenge. Existing fusion methods often consider only relevance and neglect the complementarity of different modalities’ features, leading to the loss of some cross-modal complementary information. To enhance complementary information, it is believed that more comprehensive cross-modal interactions should be provided. Therefore, a fusion network for infrared and visible fusion is proposed, which is based on bilateral cross-feature interaction, termed BCMFIFuse. To obtain features in images of different modalities, we devise a two-stream network. During the feature extraction, a cross-modal feature correction block (CMFC) is introduced, which calibrates the current modality features by leveraging feature correlations from different modalities in both spatial and channel dimensions. Then, a feature fusion block (FFB) is employed to effectively integrate cross-modal information. The FFB aims to explore and integrate the most discriminative features from the infrared and visible image, enabling long-range contextual interactions to enhance global cross-modal features. In addition, to extract more comprehensive multi-scale features, we develop a hybrid pyramid dilated convolution block (HPDCB). Comprehensive experiments on different datasets reveal that our method performs excellently in qualitative, quantitative, and object detection evaluations.

ASCEND-UNet: An Improved UNet Configuration Optimized for Rural Settlements Mapping.

Different types of rural settlement agglomerations have been formed and mixed in space during the rural revitalization strategy implementation in China. Discriminating them from remote sensing images is of great significance for rural land planning and living environment improvement. Currently, there is a lack of automatic methods for obtaining information on rural settlement differentiation. In this paper, an improved encoder-decoder network structure, ASCEND-UNet, was designed based on the original UNet. It was implemented to segment and classify dispersed and clustered rural settlement buildings from high-resolution satellite images. The ASCEND-UNet model incorporated three components: firstly, the atrous spatial pyramid pooling (ASPP) multi-scale feature fusion module was added into the encoder, then the spatial and channel squeeze and excitation (scSE) block was embedded at the skip connection; thirdly, the hybrid dilated convolution (HDC) block was utilized in the decoder. In our proposed framework, the ASPP and HDC were used as multiple dilated convolution blocks to expand the receptive field by introducing a series of dilated rate convolutions. The scSE is an attention mechanism block focusing on features both in the spatial and channel dimension. A series of model comparisons and accuracy assessments with the original UNet, PSPNet, DeepLabV3+, and SegNet verified the effectiveness of our proposed model. Compared with the original UNet model, ASCEND-UNet achieved improvements of 4.67%, 2.80%, 3.73%, and 6.28% in precision, recall, F1-score and MIoU, respectively. The contributions of HDC, ASPP, and scSE modules were discussed in ablation experiments. Our proposed model obtained more accurate and stable results by integrating multiple dilated convolution blocks with an attention mechanism. This novel model enriches the automatic methods for semantic segmentation of different rural settlements from remote sensing images.

Hybrid attentive prototypical network for few-shot action recognition

Most previous few-shot action recognition works tend to process video temporal and spatial features separately, resulting in insufficient extraction of comprehensive features. In this paper, a novel hybrid attentive prototypical network (HAPN) framework for few-shot action recognition is proposed. Distinguished by its joint processing of temporal and spatial information, the HAPN framework strategically manipulates these dimensions from feature extraction to the attention module, consequently enhancing its ability to perform action recognition tasks. Our framework utilizes the R(2+1)D backbone network, coupling the extraction of integrated temporal and spatial features to ensure a comprehensive understanding of video content. Additionally, our framework introduces the novel Residual Tri-dimensional Attention (ResTriDA) mechanism, specifically designed to augment feature information across the temporal, spatial, and channel dimensions. ResTriDA dynamically enhances crucial aspects of video features by amplifying significant channel-wise features for action distinction, accentuating spatial details vital for capturing the essence of actions within frames, and emphasizing temporal dynamics to capture movement over time. We further propose a prototypical attentive matching module (PAM) built on the concept of metric learning to resolve the overfitting issue common in few-shot tasks. We evaluate our HAPN framework on three classical few-shot action recognition datasets: Kinetics-100, UCF101, and HMDB51. The results indicate that our framework significantly outperformed state-of-the-art methods. Notably, the 1-shot task, demonstrated an increase of 9.8% in accuracy on UCF101 and improvements of 3.9% on HMDB51 and 12.4% on Kinetics-100. These gains confirm the robustness and effectiveness of our approach in leveraging limited data for precise action recognition.

Multiple Instance Pathology Image Diagnosis Model based on Channel Attention and Data Augmentation

The application of machine learning in the medical field has resulted in significant advancements in computer-aided pathological diagnosis. Multiple instance learning (MIL) has emerged as a promising approach for pathological image classification, particularly in scenarios where local annotations are lacking. However, current MIL models often overlook the importance of feature weights in the channel dimension and struggle with imbalanced positive and negative data. To address these limitations, an integration of a channel attention (CA) module and an augmented data (AUG) mechanism into the MIL model is proposed, resulting in improved performance. The CA module dynamically assigns weights to example features in the channel dimension, enhancing or suppressing features adaptively. Additionally, the AUG mechanism effectively balances the distribution of positive and negative data, significantly reducing false negatives. Through ablation experiments, the contributions of the CA module and AUG mechanism in enhancing the overall model performance are analyzed. Experimental validations on the CAMELYON16/17 public pathological image datasets demonstrate that the proposed model and method outperform existing approaches, with particular emphasis on reducing false negatives.

BCLNet: Boundary contrastive learning with gated attention feature fusion and multi-branch spatial-channel reconstruction for land use classification

The fusion of optical and synthetic aperture radar (SAR) images is not only a crucial method for enhancing land use classification tasks but also forms a fundamental basis for the interpretation of multimodal remote sensing imagery. However, the visual differences caused by the distinct nonlinear radiometric properties of optical and SAR images lead to challenges in effectively integrating multimodal features using current land use classification methods, resulting in lower classification accuracy. On one hand, these approaches may be affected by the heterogeneity among different source images, leading to an inability to fully utilize the semantic content of the fused features. On the other hand, the semantic embedding space does not consider the semantic relationships between different pixels from a global perspective (i.e., across the entire dataset). To address the aforementioned challenges, we propose the boundary contrastive learning classification network (BCLNet). In this framework, we introduce the gate attention fusion (GAF) module, which selectively considers distinctiveness and consistency features in optical and SAR imagery, facilitating effective modal representation fusion. Furthermore, we introduce the multi-branch spatial channel reconstruction (MSCR) module for enhanced feature augmentation, operating in both spatial and channel dimensions with a dual-dimensional feature reconstruction selection mechanism for the fused features. Furthermore, we adopt boundary contrast learning to address the global pixel semantic space embedding problem. Our method is evaluated on the WHU-OPT-SAR dataset and a multi-class scene classification dataset (MSD) that we constructed. Compared to state-of-the-art methods, our approach demonstrates improvements in pixel classification overall accuracy (OA) and mean intersection over union (mIOU).

Segmentation Network for Multi-Shape Tea Bud Leaves Based on Attention and Path Feature Aggregation

Accurately detecting tea bud leaves is crucial for the automation of tea picking robots. However, challenges arise due to tea stem occlusion and overlapping of buds and leaves, presenting varied shapes of one bud–one leaf targets in the field of view, making precise segmentation of tea bud leaves challenging. To improve the segmentation accuracy of one bud–one leaf targets with different shapes and fine granularity, this study proposes a novel semantic segmentation model for tea bud leaves. The method designs a hierarchical Transformer block based on a self-attention mechanism in the encoding network, which is beneficial for capturing long-range dependencies between features and enhancing the representation of common features. Then, a multi-path feature aggregation module is designed to effectively merge the feature outputs of encoder blocks with decoder outputs, thereby alleviating the loss of fine-grained features caused by downsampling. Furthermore, a refined polarized attention mechanism is employed after the aggregation module to perform polarized filtering on features in channel and spatial dimensions, enhancing the output of fine-grained features. The experimental results demonstrate that the proposed Unet-Enhanced model achieves segmentation performance well on one bud–one leaf targets with different shapes, with a mean intersection over union (mIoU) of 91.18% and a mean pixel accuracy (mPA) of 95.10%. The semantic segmentation network can accurately segment tea bud leaves, providing a decision-making basis for the spatial positioning of tea picking robots.

A method for extracting buildings from remote sensing images based on 3DJA-UNet3+

Building extraction aims to extract building pixels from remote sensing imagery, which plays a significant role in urban planning, dynamic urban monitoring, and many other applications. UNet3+ is widely applied in building extraction from remote sensing images. However, it still faces issues such as low segmentation accuracy, imprecise boundary delineation, and the complexity of network models. Therefore, based on the UNet3+ model, this paper proposes a 3D Joint Attention (3DJA) module that effectively enhances the correlation between local and global features, obtaining more accurate object semantic information and enhancing feature representation. The 3DJA module models semantic interdependence in the vertical and horizontal dimensions to obtain feature map spatial encoding information, as well as in the channel dimensions to increase the correlation between dependent channel graphs. In addition, a bottleneck module is constructed to reduce the number of network parameters and improve model training efficiency. Many experiments are conducted on publicly accessible WHU,INRIA and Massachusetts building dataset, and the benchmarks, BOMSC-Net, CVNet, SCA-Net, SPCL-Net, ACMFNet, MFCF-Net models are selected for comparison with the 3DJA-UNet3+ model proposed in this paper. The experimental results show that 3DJA-UNet3+ achieves competitive results in three evaluation indicators: overall accuracy, mean intersection over union, and F1-score. The code will be available at https://github.com/EnjiLi/3DJA-UNet3Plus.

SDGSA: a lightweight shallow dual-group symmetric attention network for micro-expression recognition

Recognizing micro-expressions (MEs) as subtle and transient forms of human emotional expressions is critical for accurately judging human feelings. However, recognizing MEs is challenging due to their transient and low-intensity characteristics. This study develops a lightweight shallow dual-group symmetric attention network (SDGSA) to address the limitations of existing methods in capturing the subtle features of MEs. This network takes the optical flow features as inputs, extracting ME features through a shallow network and performing finer feature segmentation in the channel dimension through a dual-group strategy. The goal is to focus on different types of facial information without disrupting facial symmetry. Moreover, this study implements a spatial symmetry attention module, focusing on extracting facial symmetry features to emphasize further the symmetric information of the left and right sides of the face. Additionally, we introduce the channel blending technique to optimize the information fusion between different channel features. Extensive experiments on SMIC, CASME II, SAMM, and 3DB-combined mainstream ME datasets demonstrate that the proposed SDGSA method outperforms the metrics of current state-of-the-art methods. As shown by ablation experimental results, the proposed dual-group symmetric attention module outperforms classical attention modules, such as the convolutional block attention module, squeeze-and-excitation, efficient channel attention, spatial group-wise enhancement, and multi-head self-attention. Importantly, SDGSA maintained excellent performance while having only 0.278 million parameters. The code and model are publicly available at https://github.com/YZY980123/SDGSA.

FRCE: Transformer-based feature reconstruction and cross-enhancement for occluded person re-identification

Occluded person re-identification (ReID) aims to capture the same person from different camera views with partial occlusion. In real-world scenarios, any object can become a random occluder, which is still an unsettled challenge for person ReID. Previous methods used auxiliary models to locate or reconstruct the occluded parts for ReID, but these methods are affected by external models or unable to model well, leaving the difficulty unresolved. Currently, Transformer has been widely applied to occluded person ReID tasks, but due to the lack of attention to local features, these methods still have limitations. Therefore, we propose a Feature Reconstruction and Cross-Enhancement (FRCE) network. Firstly, a Cross-level Feature Reconstruction Distiller (CFRD) is designed to improve the feature richness in the recognition scope and generate higher quality identity feature mapping by reconstructing the effective recognition region of the target. Secondly, we introduce a Multi-Dimensional Local Refinement (MDLR) module to deeply excavate local details in spatial and channel dimensions, seek subtle target cues and similarity relationships at local angles, and achieve more efficient ReID. Extensive experiments on five challenging occluded and holistic person ReID datasets demonstrate the effectiveness of our approach.

Cross-Modality Person Re-Identification Method with Joint-Modality Generation and Feature Enhancement.

In order to minimize the disparity between visible and infrared modalities and enhance pedestrian feature representation, a cross-modality person re-identification method is proposed, which integrates modality generation and feature enhancement. Specifically, a lightweight network is used for dimension reduction and augmentation of visible images, and intermediate modalities are generated to bridge the gap between visible images and infrared images. The Convolutional Block Attention Module is embedded into the ResNet50 backbone network to selectively emphasize key features sequentially from both channel and spatial dimensions. Additionally, the Gradient Centralization algorithm is introduced into the Stochastic Gradient Descent optimizer to accelerate convergence speed and improve generalization capability of the network model. Experimental results on SYSU-MM01 and RegDB datasets demonstrate that our improved network model achieves significant performance gains, with an increase in Rank-1 accuracy of 7.12% and 6.34%, as well as an improvement in mAP of 4.00% and 6.05%, respectively.

Simulasi Jaringan Drainase Kawasan Desa Lubuk Suli Kec. Depati VII Kabupaten Kerinci Menggunakan Program EPA SWMM Versi 5.1

One of the areas in Kerinci Regency that is often hit by floods is Lubuk Suli Village. Flooding was caused by overflowing water from the Batang Marao river. The main objective of this research is to provide information to the relevant government about good drainage for the Lubuk Suli Village Area using existing data and planning data by simulating the capability of the Drainage Network for the Lubuk Suli Village Area using the EPA SWMM 5.1 program. The data used in this research are land use maps to determine the percentage of impervious area, rainfall data and drainage channel dimension data in the area. The rain data series used is hourly rain data. Due to the absence of hourly rainfall data at the rain station at the research location, hourly rainfall distribution was determined by changing the Intensity-Duration-Frequency (IDF) curve for a 5 year return period into a planned rainfall hyetograph using the Alternating Block Method (ABM). After all parameters have been input into EPA SWMM 5.1. Simulation can be done. The simulation quality is quite good if the continuity error for surface runoff and flow tracing is > 10%. The simulation carried out in this research is divided into 2 scenarios, namely with existing data and additional storage. From the simulation results, it was found that in the 2 scenarios, the number of flood points in the simulation for scenario 1 was 6 points and in scenario 2 there were 5 points. This shows that the drainage planning design for the Lubuk Suli Village area has not been able to overcome the existing drainage load in the area.

Innovative lightweight deep learning architecture for enhanced rice pest identification

Pest detection is a crucial aspect of rice production. Accurate and timely identification of rice pests can assist farmers in taking prompt measures for control. To enhance the precision and real-time performance of rice pest detection, this paper introduces a novel YOLOv8-SCS architecture that integrates Space-to-Depth Convolution (SPD-Conv), Context Guided block (CG block), and Slide Loss. Initially, the original algorithm’s convolutional module is improved by introducing the SPD-Conv module, which reorganises the input channel dimensions into spatial dimensions, enabling the model to capture fine-grained pest features more efficiently while maintaining a lightweight model architecture. Subsequently, the CG block module is integrated into the CSPDarknet53 to 2-Stage FPN (C2f) structure, maintaining the models lightweight nature while enhancing its feature extraction capabilities. Finally, the Binary Cross-Entropy (BCE) is refined by incorporating the Slide Loss function, which encourages the model to focus more on challenging samples during training, thereby improving the model’s generalization across various samples. To validate the effectiveness of the improved algorithm, a series of experiments were conducted on a rice pest dataset. The results demonstrate that the proposed model outperforms the original YOLOv8 in rice pest detection, achieving an mAP of 87.9%, which is a 5.7% improvement over the original YOLOv8. The model also features a 44.1% reduction in parameter count and a decrease of 11.7 GFLOPs in computational requirements, meeting the demands for real-time detection.

INA-Net: An integrated noise-adaptive attention neural network for enhanced medical image segmentation

Skin cancer, a growing health concern, poses a significant threat. Automated segmentation of skin lesions is crucial for precise clinical diagnosis and treatment. However, challenges such as hair interference, vascular occlusion, and ambiguous lesion boundaries in dermoscopic images complicate accurate segmentation. Recent research has focused on capturing multiscale-enhanced global information across spatial and channel dimensions to improve the segmentation of ambiguous boundaries. Despite these advancements, performance remains suboptimal in the presence of complex disturbances. To address this, we propose INA-Net, an Integrated Noise-Adaptive Attention Neural Network for enhanced medical image segmentation. INA-Net proposes a lightweight noise-range attention module that integrates local and simulated noise features with global information to improve robustness against complex mixed noises. Additionally, our proposed Edge-aware Spatial Attention (ESA) and Multi-scale Channel Attention (MCA) enhance feature information and boundary perception. The proposed Dynamic Noise Encoding module and Fourier Wavelet Analysis (FW-Parser) further improve robustness by handling high-frequency noise components. INA-Net effectively manages hair interference, vascular occlusion, and ambiguous lesion boundaries, thereby providing critical support for accurate lesion identification. Evaluations on the ISIC2016, ISIC2018, and PH2 datasets demonstrate outstanding performance, with IOU scores of 87.53%, 84.74%, and 85.30%, respectively. These results surpass those of other models with comparable complexity and size.

Complex-Valued 2D-3D Hybrid Convolutional Neural Network with Attention Mechanism for PolSAR Image Classification

The recently introduced complex-valued convolutional neural network (CV-CNN) has shown considerable advancements for polarimetric synthetic aperture radar (PolSAR) image classification by effectively incorporating both magnitude and phase information. However, a solitary 2D or 3D CNN encounters challenges such as insufficiently extracting scattering channel dimension features or excessive computational parameters. Moreover, these networks’ default is that all information is equally important, consuming vast resources for processing useless information. To address these issues, this study presents a new hybrid CV-CNN with the attention mechanism (CV-2D/3D-CNN-AM) to classify PolSAR ground objects, possessing both excellent computational efficiency and feature extraction capability. In the proposed framework, multi-level discriminative features are extracted from preprocessed data through hybrid networks in the complex domain, along with a special attention block to filter the feature importance from both spatial and channel dimensions. Experimental results performed on three PolSAR datasets demonstrate our present approach’s superiority over other existing ones. Furthermore, ablation experiments confirm the validity of each module, highlighting our model’s robustness and effectiveness.

New Electrochemical Channel Flow Cell Design for Electrochemical Studies at Elevated Temperatures and Pressures

Temperature exerts a considerable influence on the efficiency of chemical and electrochemical processes, and it is expected that studies under wide temperature and pressure (T and p) conditions can significantly impact fields of technological interest such as energy storage and conversion, water treatment, waste conversion, metal recovery, and electrosynthesis, among others.1 The electrochemical studies conducted by Bard’s group in near-critical and supercritical aqueous solutions,2 as well as the contributions made by MacDonald’s and Lvov’s3,4 groups on corrosion and pH determination in hydrothermal systems, offer valuable insights into the effects of temperature and pressure on electrochemical reactions under extreme conditions and construction materials and electrode designs. Other studies in the field, though they did not reach such harsh conditions, made valuable contributions by proposing innovative hydrodynamic electrode systems, from channel cells for studies up to 110 oC,5 a rotating disc electrode for temperatures up to 150 oC,6 and even an imping jet electrode able to reach 210 oC.7 Unfortunately, these studies were rarely expanded to more than one or two systems.In this work, various hydrodynamic electrode configurations have been examined to conclude that the channel flow cell design presents several advantages over other systems for high T and P applications. These cells can provide consistent and reproducible mass transport conditions across a broad range of flow rates and allow channel dimension adjustments to tackle diverse technological challenges. Furthermore, this configuration aligns well with spectroscopic techniques like Raman/SERS (surface-enhanced Raman spectroscopy). COMSOL Multiphysics was used when designing the cell prototype, and validation of the model was carried out using data from previous studies.8 The high T,p cell uses disposable thin-film electrodes (TFEs) patterned onto sapphire wafers through nanofabrication methods; a significant amount of time has been invested in testing the performance of the TFEs up to at least 150°C. These results will also be presented along with experiments conducted with the new channel cell under ambient conditions. References (1) Giovanelli, D.; Lawrence, N. S.; Compton, R. G., Electroanalysis (New York, N.Y.) 2004, 16 (10), 789-810.(2) Liu, C.-y.; Snyder, S. R.; Bard, A. J. J. Phys. Chem.B 1997, 101 (7), 1180-1185.(3) Macdonald, D. D. Corrosion 2013, 34 (3), 75-84.(4) Balashov, V. N.; Fedkin, M. V.; Lvov, S. N., J. Electrochem. Soc. 2009, 156 (7), C209.(5) Wang, H.; Jusys, Z.; Behm, R. J.; Abruña, H. D., J. Electroanal. Chem. 2021, 896, 115251.(6) Fleige, M. J.; Wiberg, G. K.; Arenz, M., Rev Sci Instrum 2015, 86 (6), 064101.(7) Trevani, L. N.; Calvo, E.; Corti, H. R., Electrochem. Commun 2000, 2 (5), 312-316.(8) Moorcroft, M. J.; Lawrence, N. S.; Coles, B. A.; Compton, R. G.; Trevani, L. J. Electroanal. Chem. 2001, 506 (1), 28-33.

Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.

Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI's image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87-0.90; s-ADC 0.88-0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85-0.86, s-ADC 0.85-0.93; Reader2 ICC: b-ADC 0.86-0.87, s-ADC 0.89-0.92, respectively). ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making.

FFA-BiGRU: Attention-Based Spatial-Temporal Feature Extraction Model for Music Emotion Classification

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method.

MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.

Jaw cyst is a fluid-containing cystic lesion that can occur in any part of the jaw and cause facial swelling, dental lesions, jaw fractures, and other associated issues. Due to the diversity and complexity of jaw images, existing deep-learning methods still have challenges in segmentation. To this end, we propose MARes-Net, an innovative multi-scale attentional residual network architecture. Firstly, the residual connection is used to optimize the encoder-decoder process, which effectively solves the gradient disappearance problem and improves the training efficiency and optimization ability. Secondly, the scale-aware feature extraction module (SFEM) significantly enhances the network's perceptual abilities by extending its receptive field across various scales, spaces, and channel dimensions. Thirdly, the multi-scale compression excitation module (MCEM) compresses and excites the feature map, and combines it with contextual information to obtain better model performance capabilities. Furthermore, the introduction of the attention gate module marks a significant advancement in refining the feature map output. Finally, rigorous experimentation conducted on the original jaw cyst dataset provided by Quzhou People's Hospital to verify the validity of MARes-Net architecture. The experimental data showed that precision, recall, IoU and F1-score of MARes-Net reached 93.84%, 93.70%, 86.17%, and 93.21%, respectively. Compared with existing models, our MARes-Net shows its unparalleled capabilities in accurately delineating and localizing anatomical structures in the jaw cyst image segmentation.

Generalized denoising network LGCT-Net for various types of ESPI wrapped phase patterns

In this paper, we propose a generalized network based on our proposed Local-Global Channel Transformer (LGCT) module for denoising various types of ESPI wrapped phase patterns (including low-density, medium-density, high-density, variable-density, and discontinuous phase patterns). The Conv + BN + ReLU layer consists of convolution (Conv), batch normalization (BN), and the rectified linear unit (Relu) in series. The generalized network LGCT-Net interleaves four LGCT modules with five Conv + BN + ReLU layers in a dense connection manner. We propose the LGCT Module by stacking three Dilated-Group Convolution blocks (DGC block), a Contextual Transformer block (CoT block), and an Efficient Channel Attention block (ECA block). The LGCT module simultaneously leverages the local context extraction capability of convolutions and the powerful global information extraction capability of a transformer. Additionally, it performs feature extraction in both spatial and channel dimensions. We also create a diverse ESPI wrapped phase pattern denoising dataset with various densities, shapes, noise levels, and discontinuity. We successfully train the LGCT-Net without any preprocessing or postprocessing steps. We evaluate the performance of our method on simulated and experimental ESPI wrapped phase patterns with discontinuity and different densities. Then we compare it with previously published denoising methods PEARLS, HDCNN, ADCNN, and DBDNet quantitatively and qualitatively. The results show that our method facilitates the reduction of speckle noise and the enhancement of fine details while preserving structure and shape, outperforming the compared methods. In the end, we apply our method to dynamic measurements of nuclear graphite ESPI phase patterns at different times. And then performing phase unwrapping on the filtered phase patterns, we achieve successful results.

Multi-dimensional attention-aided transposed ConvBiLSTM network for hyperspectral image super-resolution

Hyperspectral (HS) image always suffers from the deficiency of low spatial resolution, compared with conventional optical image types, which has limited its further applications in remote sensing areas. Therefore, HS image super-resolution (SR) techniques are broadly employed in order to observe finer spatial structures while preserving the spectra of ground covers. In this paper, a novel multi-dimensional attention-aided transposed convolutional long-short term memory (LSTM) network is proposed for single HS image super-resolution task. The proposed network employs the convolutional bi-directional LSTM for the purpose of local and non-local spatial–spectral feature explorations, and transposed convolution for the purpose of image amplification and reconstruction. Moreover, a multi-dimensional attention module is proposed, aiming to capture the salient features on spectral, channel, and spatial dimensions, simultaneously, to further improve the learning abilities of network. Experiments on four commonly-used HS images demonstrate the effectiveness of this approach, compared with several state-of-the-art deep learning-based SR methods.