# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 1 Introduction
utilized the Vision Transformer (ViT-B/16) in combination with an innovative asymmetric loss function, resulting in an identification accuracy exceeding 92.36% on a self-collected dataset, thereby significantly enhancing garbage data classification performance [14]. [PERSON] et al. proposed a convolutional neural network (CNN) hybrid model that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) with transfer learning, achieving a classification accuracy of 96.78% on the TrashNet dataset [15]. [PERSON] and [PERSON] adopted the EfficientNetB3 model for fine-grained classification of garbage bags, paper bags, and plastic bags, reaching an impressive accuracy of 97% [16]. [PERSON] et al. improved the overall discriminative power of their model by fusing the local features extracted by ResNet with the global information captured by the Vision Transformer, achieving an accuracy of 96.54% [17]. [PERSON] et al. introduced the Garbage Classifier Deep Neural Network (GCDN-Net) deep neural network model, which demonstrated excellent performance in both single-label and multi-label classification of urban recyclable garbage [18]. Finally, [PERSON] et al. realized complementary model strengths by integrating ResNet, MobileNetV2, and You Only Look Once version 5 (YOLOv5), achieving an image classification accuracy of 98% [19]. To further enhance the model's feature aggregation and information propagation efficiency, researchers have embarked on new explorations in network architecture and attention mechanisms. [PERSON] et al. augmented the Inception-V3 framework with Inverted Bottleneck and Contextual Transformer modules, thereby bolstering the model's capability to manage complex backgrounds [20]. [PERSON] et al. optimized the structure of ShuffleNet V2 and integrated it with the MobileViT model to improve cross-channel information exchange, which in turn elevated garbage classification accuracy [21]. Furthermore, [PERSON] et al. introduced the Coordinate Attention (CA) mechanism into the YOLOv5 framework, complemented by data augmentation and adversarial sample generation, which significantly improved the recognition accuracy of garbage images [22]. [PERSON] et al. embedded multiple attention mechanisms within the YOLOv8 architecture and systematically assessed the impact of each module on overall performance [23]. In addition, [PERSON] et al. developed an automated garbage classification system based on an enhanced YOLOv5, achieving further gains in detection precision through structural optimization [24]. To further enhance model robustness and generalization in complex scenarios, several studies have incorporated advanced optimization algorithms, dynamic construction strategies, and ensemble methods. [PERSON] et al. generated image embeddings using Inception V3 and combined them with Extreme Gradient Boosting (XGBoost v1.6.2) to realize efficient classification [25]. Moreover, [PERSON] et al. explored an ensemble approach that integrated graph neural networks and recurrent neural networks based on feature extraction from VGG16 and ResNet50, effectively capturing the spatiotemporal characteristics of plastic garbage and achieving a classification accuracy of 81% [26]. [PERSON] et al. proposed a Dual-Branch Binarized Neural Network (DBBNN) that enhances garbage classification efficiency by refining both the network architecture and the loss function [27]. [PERSON] et al. and [PERSON] and [PERSON]. conducted targeted optimizations on Inception V3 and YOLOv8, respectively [28; 29]. [PERSON] et al. assembled a comprehensive dataset comprising 15,000 images and applied YOLOv8 to achieve a recognition accuracy of 90% [30]. [PERSON] and [PERSON] introduced a novel FConvNet by integrating convolutions to augment spatial correlation [31]. Furthermore, [PERSON] et al. developed a multi-subnetwork architecture by fusing Deconvolutional Single Shot Detector (DSSD), YOLOv4, and Faster Region-based Convolutional Neural Network (Faster-RCNN), and cascade classifiers [32], while [PERSON] et al.