# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 3 Proposed Method
### Eye Image Stream  
In the field of gaze estimation, eye feature extraction is crucial. Traditional methods often rely on single-eye image inputs and use shallow convolutional modules to construct feature extraction networks, aiming to improve computational efficiency, simplify training,  
Figure 1: The overall network structure of MSMI-Net consists of an eye image stream, where features are extracted through multiple layers of ConvNext modules, and a fusion stream, where low-dimensional features are extracted through convolution and feature fusion. and alleviate overfitting. However, shallow convolutional networks have limitations in capturing complex patterns and higher-order features, making them ill-suited for gaze estimation tasks in complex environments. Compared to deep networks, their feature extraction capabilities are limited, making it difficult to effectively mine the deep structure and high-dimensional features of the data, which impacts the accuracy of gaze direction prediction. Considering the limitations of shallow convolutional modules, this study adopts an efficient stack of ConvNext modules in the eye image stream to build a deep convolutional neural network structure. Task-specific improvements have been made to the ConvNext Block, as shown in Figure 2. The ConvNext module is inspired by the optimization of the ResNet structure by the Swin Transformer, maintaining the simplicity of the standard ConvNext while avoiding the introduction of attention mechanisms. Its design combines macro-structural optimizations, the grouped convolution technique from ResNeXt, the inverted bottleneck structure, the use of large convolutional kernels, and diversified strategies at the micro level. To validate the advantages of ConvNext in eye feature extraction, this paper compares the performance of different backbone networks through experiments, demonstrating the superiority of ConvNext. In the structural design of the improved ConvNext Block, this paper takes into account that eye images not only contain the eyeball itself but also the contour information around the eye, which is of great value for the gaze estimation task. Therefore, the ConvNext Block adopts a \(7\times 7\) large convolutional kernel to expand the receptive field at the same level in order to capture a wider range of contextual information while reducing the model parameters without sacrificing performance. Following the large convolutional kernel, two \(1\times 1\) convolutional kernels are specifically used to extract local features, which helps the model capture detailed information in the eye images and enhances the model's ability to express features of different sizes, providing richer visual cues for gaze estimation. Moreover, due to individual differences in eye appearance, the combination of large and small convolutional kernels in the ConvNext Block can effectively improve the model's adaptability and generalization ability to these differences. This multi-scale feature extraction strategy enables the network to better handle eye images from different individuals. In the ConvNext network, inspired by the downsampling design of the Swin Transformer, the Down Sample module introduces grouped convolution and effectively reduces the spatial size of feature maps using a \(2\times 2\) convolution with a stride of 2. This design optimizes computational efficiency and enhances model stability and performance by incorporating Layer Normalization (LN). This approach is particularly crucial for gaze estimation tasks, as it expands the receptive field while reducing computational overhead, enabling better capture of key features in eye images. The independent downsampling  
Figure 2: Eye Image Stream Detailed Structure Diagram, \(F\)I and \(Fr\) represent the left and right eye image features learned through the eye stream. module not only decreases computational complexity but also enhances the model's generalization ability to complex visual scenes, thereby improving the accuracy and robustness of gaze estimation.