GaborNeXt: a low-level visual area encoding model based on convolution kernel optimization-improvement of Gabornet

The Gabor wavelet is widely used to simulate the receptive fields of simple cells in the low–level visual cortex (such as V1, V2, and V3) of the human visual system. Based on this, end-to-end encoding models have achieved advanced encoding results in the low–level visual cortex. However, most current end-to-end encoding models are lightweight models with relatively simple structures and few parameters. This limitation may cause the models to perform poorly in processing detailed features of different frequencies and directions in complex Gabor feature maps. In this paper, a novel visual coding model GaborNeXt based on Gabor features is proposed. The model utilizes ConvNeXt convolutional layers to group independent convolutional kernels for convolutional operations and concatenates the outputs of each group to enhance nonlinear expressive power. We conducted experiments on the NSD (Natural Scenes Dataset) and the results demonstrate that our model outperforms the baseline models in encoding accuracy across several the low-level visual cortex. Additionally, we compared the effects of various Gabor convolutional layer kernel sizes on model performance through ablation experiments and found that using larger convolutional kernels in the Gabor convolutional layer has a positive impact on the model's performance.