# Fusion Classification Method Based on Audiovisual Information Processing
## 2 Related Work  
### Single-Modal Target Classification  
Image classification, as a popular research topic in the field of computer vision, has developed rapidly in recent years. It employs a series of complex algorithms and models to deeply analyze various features in images, such as color, texture, shape, and edges, thereby accurately extracting the core information of the image. This information is then processed and transformed into a form that machines can understand and process. For example, in the ImageNet challenge in 2012, the introduction of the AlexNet model [22] achieved an overwhelming victory. Its network structure significantly improved the accuracy of image classification and solved the problems of gradient vanishing and overfitting during model training in deep learning. Subsequently, the introduction of GoogLeNet [23], VGG [9], and ResNet [7] accelerated progress in the field of image classification. They all optimized the network structure to enhance the model's ability to process images and improve its classification accuracy. In 2020, Google proposed Vision Transformer (ViT) [24], which achieved end-to-end modeling of high-resolution images through a global attention mechanism, significantly enhancing the performance of image classification tasks. In 2025, [PERSON] [25] introduced the DINOv2 model, which employs self-supervised learning strategies to extract universal visual features without labeled data, significantly improving the adaptability of cross-domain image classification.  
Sound classification, another research hotspot in the field of target classification, has also been widely explored by researchers. Sound signals possess unique spatiotemporal features that can provide effective information in complex environments. Therefore, extracting and analyzing these features has become the key to research. Deep learning models, trained on large-scale data, can learn more robust feature representations, thereby demonstrating higher robustness in complex environments with noise, accents, and varying speech rates. In 2019, [PERSON] [26] and others proposed a large-scale pre-trained audio neural network (PANNS) for audio pattern classification. This method introduced the Wavegram-Log-Mel-CNN architecture, which combines log-mel spectrograms and waveforms as input features, significantly enhancing the performance of audio pattern classification. In 2020, [PERSON] [27] and others proposed the ECAPA_TDNN method, which introduced the Squeeze-and-Excitation (SE) module to enhance the model's ability to focus on important feature channels and aggregate features from multiple layers. By leveraging complementary information from different levels, this method improved the model's feature representation capability and enhanced sound classification performance.  
Despite the significant breakthroughs achieved in sound classification through deep learning, monomodal sound classification exhibits poor robustness in complex environments. Moreover, under the interference of noise, deep learning-based sound classification techniques may fail to classify targets. In the field of deep learning-based image classification, reliance on large-scale data annotation and extensive model training is significant. Additionally, under conditions of varying lighting and target occlusion, robustness tends to decline, leading to a substantial decrease in the accuracy of image target classification. Therefore, to enhance classification robustness, it is necessary to introduce other modalities to assist in target classification.