Modulation Of Aggregation Research Articles

WaveFrSnow: Comprehensive perception wavelet transform frequency separation transformer for image snow removal

Types of snow degradation are complex and diverse. Snow removal often requires the construction of sufficient visual representations. Although convolution-based methods perform well in local perception, they struggle to model globally. On the other hand, methods based on self-attention can capture long-range dependencies but often overlook local information and texture details. In this paper, we proposed a hybrid network called WaveFrSnow, aimed at enhancing the performance of single-image snow removal by combining the advantages of convolution and cross-attention. Firstly, we introduced a frequency-separation cross-attention mechanism based on wavelet transform (WaveFrSA) to enhance the global and texture representations of snow removal. Specifically, frequency-separated attention perceives the texture in the high-frequency branch, captures global information in the low-frequency branch, and introduces convolution to obtain local features. In addition, we constructed local representations through efficient convolutional encoder branches. Furthermore, we develop a Multi-Scale Degradation Aggregation (MSDA) module to integrate rich implicit degradation features. Based on the MSDA module, a Degradation Area Restoration (DAR) network is constructed, aiming to achieve high-quality image restoration following the snow removal process. Taken together, comprehensive experimental results on multiple publicly available datasets demonstrate the superiority of the proposed method over the state-of-the-art method. Additionally, the desnowing results effectively improve the accuracy of downstream vision tasks. The code and datasets in this study are available at https://github.com/dxw2000/WaveFrSnow.

STDAN: Deformable Attention Network for Space-Time Video Super-Resolution.

The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction. To address these issues, in this article, we propose a deformable attention network called STDAN for STVSR. First, we devise a long short-term feature interpolation (LSTFI) module that is capable of excavating abundant content from more neighboring input frames for the interpolation process through a bidirectional recurrent neural network (RNN) structure. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction. Experimental results on several datasets demonstrate that our approach outperforms state-of-the-art STVSR methods. The code is available at https://github.com/littlewhitesea/STDAN.

A frequency selection network for medical image segmentation

Existing medical image segmentation methods may only consider feature extraction and information processing in spatial domain, or lack the design of interaction between frequency information and spatial information, or ignore the semantic gaps between shallow and deep features, and lead to inaccurate segmentation results. Therefore, in this paper, we propose a novel frequency selection segmentation network (FSSN), which achieves more accurate lesion segmentation by fusing local spatial features and global frequency information, better design of feature interactions, and suppressing low correlation frequency components for mitigating semantic gaps. Firstly, we propose a global-local feature aggregation module (GLAM) to simultaneously capture multi-scale local features in the spatial domain and exploits global frequency information in the frequency domain, and achieves complementary fusion of local details features and global frequency information. Secondly, we propose a feature filter module (FFM) to mitigate semantic gaps when we conduct cross-level features fusion, and makes FSSN discriminatively determine which frequency information should be preserved for accurate lesion segmentation. Finally, in order to make better use of local information, especially the boundary of lesion region, we employ deformable convolution (DC) to extract pertinent features in the local range, and makes our FSSN can focus on relevant image contents better. Extensive experiments on two public benchmark datasets show that compared with representative medical image segmentation methods, our FSSN can obtain more accurate lesion segmentation results in terms of both objective evaluation indicators and subjective visual effects with fewer parameters and lower computational complexity.

Cross-Image Pixel Contrasting for Semantic Segmentation.

This work studies the problem of image semantic segmentation. Current approaches focus mainly on mining "local" context, i.e., dependencies between pixels within individual images, by specifically-designed, context aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization objectives (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive algorithm, dubbed as PiCo, for semantic segmentation in the fully supervised learning setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely studied before. Our training algorithm is compatible with modern segmentation solutions without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCRNet, SegFormer, Segmenter, MaskFormer) and backbones (i.e., MobileNet, ResNet, HRNet, MiT, ViT), our algorithm brings consistent performance improvements across diverse datasets (i.e., Cityscapes, ADE20 K, PASCAL-Context, COCO-Stuff, CamVid). We expect that this work will encourage our community to rethink the current de facto training paradigm in semantic segmentation.

Evidential Multi-Source-Free Unsupervised Domain Adaptation.

Multi-Source-Free Unsupervised Domain Adaptation (MSFUDA) requires aggregating knowledge from multiple source models and adapting it to the target domain. Two challenges remain: 1) suboptimal coarse-grained (domain-level) aggregation of multiple source models, and 2) risky semantics propagation based on local structures. In this article, we propose an evidential learning method for MSFUDA, where we formulate two uncertainties, i.e. Evidential Prediction Uncertainty (EPU) and Evidential Adjacency-Consistent Uncertainty (EAU), respectively for addressing the two challenges. The former, EPU, captures the uncertainty of a sample fitted to a source model, which can suggest the preferences of target samples for different source models. Based on this, we develop an EPU-Based Multi-Source Aggregation module to achieve fine-grained, instance-level source knowledge aggregation. The latter, EAU, provides a robust measure of consistency among adjacent samples in the target domain. Utilizing this, we develop an EAU-Guided Local Structure Mining module to ensure the trustworthy propagation of semantics. The two modules are integrated into the Evidential Aggregation and Adaptation Framework (EAAF), and we demonstrated that this framework achieves state-of-the-art performances on three MSFUDA benchmarks.

FedNor: A robust training framework for federated learning based on normal aggregation

Addressing data security and data silo issues in Edge Intelligence, this paper proposes a Byzantine-resilient framework (FedNor) for Federated Learning (FL). FedNor integrates robust statistical methods with personalized FL strategies to enhance resilience against malicious updates while maintaining model generalization capabilities. The framework comprises two key components: the Robust Normal Aggregation (RN) module and the Personalized Fusion (PF) module. The RN module employs normality tests to identify and rectify anomalous updates, thereby ensuring the integrity and quality of model updates. Concurrently, the PF module incorporates data distribution considerations when integrating global and local models to optimize model security and accuracy. Experimental results demonstrate FedNor's effectiveness in mitigating eight distinct poisoning attacks on the MNIST datasets, with minimal accuracy degradation ranging from 0.42% to 1.96%. Furthermore, FedNor limits the backdoor attack success rate on the CIFAR-10 datasets to below 20%, while maintaining accuracy comparable to personalized FL schemes.

Ancient paintings inpainting based on dual encoders and contextual information

Deep learning-based inpainting models have achieved success in restoring natural images, yet their application to ancient paintings encounters challenges due to the loss of texture, lines, and color. To address these issues, we introduce an ancient painting inpainting model based on dual encoders and contextual information to overcome the lack of feature extraction and detail texture recovery when restoring ancient paintings. Specifically, the proposed model employs a gated encoding branch that aims to minimize information loss and effectively capture semantic information from ancient paintings. A dense multi-scale feature fusion module is designed to extract texture and detail information at various scales, while dilated depthwise separable convolutions are utilized to reduce parameters and enhance computational efficiency. Furthermore, a contextual feature aggregation module is incorporated to extract contextual features, enhancing the overall consistency of the inpainting results. Finally, a color loss function is introduced to ensure color consistency in the restored area, harmonizing it with the surrounding region. The experimental results indicate that the proposed model effectively restores the texture details of ancient paintings, outperforming other methods both qualitatively and quantitatively. Additionally, the model is tested on real damaged ancient paintings to validate its practicality and efficacy.

BMCS-Net: A Bi-directional multi-scale cascaded segmentation network based on transformer-guided feature Aggregation for medical images

convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to loss of localized details. Transformer has naturally excellent ability to model the global features and long-range correlations of the input information, which is strongly complementary to the inductive bias of CNNs. In this paper, a novel Bi-directional Multi-scale Cascaded Segmentation Network, BMCS-Net, is proposed to improve the performance of medical segmentation tasks by aggregating these features obtained from Transformers and CNNs branches. Specifically, a novel feature integration technique, termed as Two-stream Cascaded Feature Aggregation (TCFA) module, is designed to fuse features in two-stream branches, and solve the problem of gradual dilution of global information in the network. Besides, a Multi-Scale Expansion-Aware (MSEA) module based on the convolution of feature perception and expansion is introduced to capture context information, and further compensate for the loss of details. Extensive experiments demonstrated that BMCS-Net has an excellent performance on both skin and Polyp segmentation datasets.

Revisiting 3D visual grounding with Context-aware Feature Aggregation

3D visual grounding is the task of accurately locating objects in a three-dimensional scene based on textual descriptions. Current approaches mainly depend on downsampling and extracting the point features for fusion with text features. However, the main challenges of these methods are the poor point feature resolution and limited local context during multi-modal fusion, causing visual-linguistic misalignment, particularly for small objects described in the text. The intuitive solution is to get additional object-related point features, gathering more contextual information to augment the representation capability of multimodal features, thereby promoting the representation capabilities of multimodal features. Based on this, we introduce a novel 3D visual grounding framework named Context-aware Feature Aggregation (CFA). The CFA framework includes two key modules: (1) Point Augmented Aggregation Module (PAM), designed to compensate for downsampling-induced information loss by augmenting sampled points with neighboring context for more discriminative features; and (2) Dual Contextual Grouping Attention Module (DCGAM), which iteratively refines features and geometry coordinates from PAM, capturing more global context. We assess the performance of our CFA framework on two point-based datasets: ScanRefer and Nr3D/Sr3D. The CFA framework exhibits efficiency in 3D visual grounding, surpassing the performance of previous methods by experimental results.

Gca-pvt-net: group convolutional attention and PVT dual-branch network for oracle bone drill chisel segmentation

Oracle bones (Obs) are a significant carrier of the shang dynasty civilization, primarily consisting of tortoise shells and animal bones, through the study of which we can gain a deeper understanding of the political, economic, religious, and cultural aspects of the shang dynasty. The oracle bone drill chisel (Obdc) is considered an essential non-textual material. The segmentation of Obdc assists archaeologists determine the approximate age of the Obs, which possesses considerable research value. However, the breakage of thousands of years of underground buried Obs, the blurring of the edges of the area burned by the Obdc, the different shapes, and the inconsistent number have brought challenges to the accurate segmentation of the Obdc. In this article, we propose a group convolutional attention and pvt dual-branch network (GCA-PVT-Net) for Obdc segmentation. To our knowledge, this paper is the first to research the automatic segmentation of Obdc. It is a hybrid Convolutional neural network (CNN) and Transformer framework. The work offers the following contributions: (1) The Obdc images are labeled based on the delineation criteria of different drill chisel (DC) shapes to create the Obdc dataset. (2) A convolutional attention module (CAM) is proposed as both an encoder and decoder. The feature extraction process, which effectively integrates global and local information, ensures better modeling of long-term correlations in images while preserving details. (3) A channel feature aggregation module (CFAM) is designed to enhance the effective integration of channel features, enabling feature fusion across various branches and at different levels. (4) The edge deep supervision strategy is applied to smooth the jagged edge of the predicted images at the decoder’s end. Extensive experiments on the Obdc dataset show that GCA-PVT-Net outperforms other state-of-the-art (SOTA) methods. The comparative experimental results show that the edge accuracy and segmentation accuracy of the model reach the top 1.

A Novel Global-Local Feature Aggregation Framework for Semantic Segmentation of Large-Format High-Resolution Remote Sensing Images

In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet.

SML: A Skeleton-based multi-feature learning method for sign language recognition

Sign language recognition (SLR) is an effective solution to communication barriers experienced by hearing and vocally impaired individuals with other communities. Its applications extend to human–robot interaction (HRI), virtual reality (VR), and augmented reality (AR). However, the diverse nature of sign languages, stemming from varying user habits and geographical regions, poses significant challenges. To address these challenges, we propose the Skeleton-based Multi-feature Learning method (SML). This method comprises a Multi Feature Aggregation (MFA) module, designed to capture the inherent relationships between different skeleton-based features, enabling effective fusion of complementary information. Furthermore, we propose the Self knowledge distillation Guided Adaptive Residual Decoupled Graph Convolutional Network (SGAR-DGCN) for feature extraction. SGAR-DGCN consists of three components: a Self Knowledge Distillation (SKD) mechanism to enhance model training, convergence, and accuracy; a DGCN-Block, incorporating Decoupled GCN and Spatio Temporal Channel attention (STC) for efficient feature extraction; and an Adaptive Residual Block (ARes-Block) for cross-layer information fusion. Experimental results demonstrate that our SML method outperforms state-of-the-art approaches on the WLASL (55.85%) and AUTSL (96.85%) datasets, solely utilizing skeleton data. Code is available at https://github.com/DzwFine37/SML.

Collaborative trajectory representation for enhanced next POI recommendation

Point-of-Interest (POI) recommendation stands as the cornerstone within a variety of location-based applications and services that intend to anticipate upcoming movements that users may be interested in. The current state-of-the-art methods have effectively explored spatio-temporal contextual features and users’ long-term and short-term preference patterns. Nevertheless, most existing work lacks the ability to effectively capture group movement patterns from trajectory collaboration. Additionally, they pay close attention to the accuracy of personalized recommendations, neglecting recommendation diversity, which refers to offering broader location options that extend beyond a user’s typical preferences, thereby avoiding excessive homogeneity or repetition. To address these gaps, this study proposes a Collaborative Trajectory Representation model (CTRNext), which enhances the diversity of recommendations while maintaining the precision of personalized preferences. To be specific, we first design two trajectory embedding layers to extract joint semantic interactions and the explicit spatiotemporal context-aware representation. Then, a trajectory semantic similarity calculation module that captures collaborative signals from potentially similar-minded users and eliminates barriers caused by trajectory length is proposed. Next, the implicit correlation and further updated representation between different check-in records are achieved through a multi-head self-attention aggregation module. Finally, we put forward a dual-driven user preference matching module to generate the preference-based next POI recommendation while enhancing diversity. Our approach demonstrates its remarkable recommendation accuracy through extensive experimentation on four real-world datasets, surpassing the performance of state-of-the-art methodologies.

Pest-ConFormer: A hybrid CNN-Transformer architecture for large-scale multi-class crop pest recognition

Crop pests are acknowledged as the major factors in reducing the yield and quality of agricultural production worldwide. It is an urgent necessity to recognize crop pests accurately to protect the crop in the early stage to reduce the loss for the agricultural economy. Due to the ecological characteristics of the crop pests and the complex background in fields, the crop pests show high inter-class similarity and significant intra-class variation in external morphology appearance, which makes current recognition methods suffer from low classification accuracy and poor generalization ability in complex natural environment recognition tasks. To tackle this problem, a hybrid convolutional neural network and transformer-based model, namely Pest-ConFormer, featured with multi-scale weakly supervised feature selection mechanisms is proposed, which has shown excellent multi-scale discriminative feature extraction in fine-grained visual classification (FGVC) tasks. This method employs a hybrid convolution-transformer encoder architecture pre-training in a self-supervised masked autoencoder manner as a backbone to learn pests’ highly discriminative features across various scales. Next, a dual-path feature aggregation structure with a top-down FPN-like feature pathway and a bottom-up PANet-like feature pathway based on attention mechanisms is designed to learn high-level global context information and low-level local detailed feature representation. Thirdly, a fine-grained classification module using weakly supervised learning is introduced to select the discriminative feature points in different pyramidal levels. Then, these feature points are fed into a graph convolutional network to accomplish classification. Several experiments are conducted on the large-scale multi-class IP102 benchmark dataset, and the proposed method achieves an accuracy of 77.81 % regarding crop pest recognition. The experimental results indicate that our approach outperforms other state-of-the-art methods by nearly 2 percent points, demonstrating that the proposed hybrid architecture with dual-path feature aggregation and fine-grained classification modules can be more effective in the crop pest recognition field than CNN-based methods and can be deployed in the practical natural environment. The source code will be available at https://github.com/mwfang/pestconformer.

Three-stage polyp segmentation network based on reverse attention feature purification with Pyramid Vision Transformer

Colorectal polyps serve as potential precursors of colorectal cancer and automating polyp segmentation aids physicians in accurately identifying potential polyp regions, thereby reducing misdiagnoses and missed diagnoses. However, existing models often fall short in accurately segmenting polyps due to the high degree of similarity between polyp regions and surrounding tissue in terms of color, texture, and shape. To address this challenge, this study proposes a novel three-stage polyp segmentation network, named Reverse Attention Feature Purification with Pyramid Vision Transformer (RAFPNet), which adopts an iterative feedback UNet architecture to refine polyp saliency maps for precise segmentation. Initially, a Multi-Scale Feature Aggregation (MSFA) module is introduced to generate preliminary polyp saliency maps. Subsequently, a Reverse Attention Feature Purification (RAFP) module is devised to effectively suppress low-level surrounding tissue features while enhancing high-level semantic polyp information based on the preliminary saliency maps. Finally, the UNet architecture is leveraged to further refine the feature maps in a coarse-to-fine approach. Extensive experiments conducted on five widely used polyp segmentation datasets and three video polyp segmentation datasets demonstrate the superior performance of RAFPNet over state-of-the-art models across multiple evaluation metrics.

SWFormer: A scale-wise hybrid CNN-Transformer network for multi-classes weed segmentation

Weeds in rapeseed field are an important factor in crop yield reduction and economic loss. Thus, Precision Agriculture is an important task for sustainable agriculture and weed management. At present, deep learning techniques have shown great potential for image-based detection and classification in various crops and weeds. However, the inherent limitations of traditional convolutional neural networks pose significant challenges due to the locally similarity of weeds and crops in color, shape and texture. To address this issue, we introduce SWFormer, a scale-wise hybrid CNN-Transformer network. SWFormer leverages the distinct strengths of both convolutional and transformer architectures. Convolutional structures excel at extracting short-range dependency information among pixels, whereas transformer structures are adept at capturing global dependency relationships. Additionally, we propose two innovative modules. Firstly, the Scale-wise Cascade Convolution (SWCC) module is designed to capture multiscale features and expand the receptive field. Secondly, the Adaptive Semantic Aggregation (ASA) module facilitates adaptive and effective information fusion across two distinct feature maps. Our experiments were conducted on the publicly available cropandweed dataset and SB20 dataset. it yields improved performance over other mainstream segmentation models. Specifically, SWFormer with 52.33M/527.51GFLOPs achieves an mAP of 76.54% and an accuracy of 83.95% on the cropandweed dataset. For the SB20 dataset, it attains an mAP of 61.24% and an accuracy of 79.47%. Overall, the evaluation clearly demonstrates our proposed SWFormer is conducive to promoting further research in the area of Precision Agriculture.

Exploitation of the nitro- and/or 4-Trifluoromethyl-decorated phenyl fragment to develop small inhibitors of Alpha-Syn fibril aggregation

Here, we report new 2-nitro and/or 4-trifluoromethylphenyl-based small molecules developed as inhibitors of alpha-Syn fibril formation. The set of eighteen compounds was inspired by well-known alpha-Syn aggregation modulators retrieved from literature. The preliminary biochemical data suggested that the two molecules out of eighteen compounds exerted activity comparable to that of reference compound SynuClean-D (SC-D, 5-nitro-6-(3-nitrophenyl)-2-oxo-4-(trifluoromethyl)-1H-pyridine-3-carbonitrile), according to Thioflavin T kinetics. Pharmacophore modelling deciphered the main structural requirements for alpha-Syn aggregation modulators. Moreover, docking and molecular dynamics simulations depicted the binding mode with the targeted alpha-Syn fibrils. The structural data of these new potential α-Syn binders might furnish additional information for understanding the mechanism of action of the ligands that specifically target the NAC domain as theranostic agents for α-synucleopathies.

SFPL: Sample-specific fine-grained prototype learning for imbalanced medical image classification

Imbalanced classification is a common and difficult task in many medical image analysis applications. However, most existing approaches focus on balancing feature distribution and classifier weights between classes, while ignoring the inner-class heterogeneity and the individuality of each sample. In this paper, we proposed a sample-specific fine-grained prototype learning (SFPL) method to learn the fine-grained representation of the majority class and learn a cosine classifier specifically for each sample such that the classification model is highly tuned to the individual’s characteristic. SFPL first builds multiple prototypes to represent the majority class, and then updates the prototypes through a mixture weighting strategy. Moreover, we proposed a uniform loss based on set representations to make the fine-grained prototypes distribute uniformly. To establish associations between fine-grained prototypes and cosine classifier, we propose a selective attention aggregation module to select the effective fine-grained prototypes for final classification. Extensive experiments on three different tasks demonstrate that SFPL outperforms the state-of-the-art (SOTA) methods. Importantly, as the imbalance ratio increases from 10 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 2.4%; as the training data decreases from 800 to 100, the improvement of SFPL over SOTA methods increases from 2.2% to 3.8%.

A generative machine learning model for the 3D reconstruction of material microstructure and performance evaluation

The 3D reconstruction is generally defined as the process of capturing the shape and appearance of real objects. By reconstructing 3D digital model from a series of 2D slices, it brings considerable convenience to visualize internal structure and decipher structure-property relation of a material. Nowadays, the 3D reconstruction is becoming a cutting-edge technique in depicting the internal structure and evaluating the physical performance of targeted materials. Recent years, generative machine learning methods, such as generative adversarial networks (GAN), have achieved tremendous success in AI-generated physical content (AIGPC). However, lots of technical challenges remain, including oversimplified models, oversized dataset requirement and inefficient convergence. These difficulties are caused by the insufficient ability to capture detailed features and the inadequacy of the generated model quality. To this end, a novel generative model is developed, which combines the multiscale features of U-net and the synthesis ability of GANs. With the help of the multiscale channel aggregation module, the hierarchical feature aggregation module and the convolutional block attention module, our model is developed to capture the features of the material microstructure better. The loss function is refined by combining the image regularization loss with the Wasserstein distance loss. In addition, the anisotropy index is adopted to measure anisotropic degree of selected samples quantitively. The results demonstrate that the 3D structures generated by the proposed model retain high fidelity with ground truth samples. With remarkable performance, the proposed model not only overtakes traditional GAN, but will shed a brilliant light on AIGPC and physical 3D reconstruction.

Meta-learning on dynamic node clustering knowledge graph for cold-start recommendation

Meta-learning has been introduced in the recommendation domain, and a possible direction to extend graph-based meta-learning is how to exploit higher-order information between nodes. The current studies primarily rely on pre-embedding methods to utilize user attributes, inadvertently overlooking the crucial co-occurrence patterns among users’ features. This oversight hinders the discovery of implicit connections between users. Furthermore, in scenarios of infrequent interactions, the reliance on initial edge information constrains connections between nodes in the same category. This constraint compromises the update capability of node embeddings, leading to reduced recommendation accuracy. In this paper, a meta-learning recommendation method with a dynamic node clustering knowledge graph(DCKG) is proposed. By introducing user attribute nodes and a triple-gated attention networks, this approach facilitates connections between users based on attribute relationships and enables the balancing of information across various pathways. Additionally, the integration of a dynamic aggregation module frees nodes of the same category from initial connection constraints, fostering more direct connections and further augmenting the propagation capability of higher-order information. The experimental results show that DCKG achieves the excellent results in the cold-start recommendation. DCKG can extend the use of higher-order features in the graph by direct connection.