Convolutional Inductive Bias
Convolutional inductive bias refers to the inherent assumptions embedded in convolutional neural networks (CNNs) that favor local spatial relationships in data processing. Current research focuses on balancing the strengths of CNNs' localized processing with the global context modeling capabilities of transformers, often by integrating both architectures or by introducing learned spatial biases into transformers. This research aims to improve model performance, particularly in data-scarce scenarios and tasks requiring both local and global feature understanding, such as image denoising and few-shot segmentation. The resulting advancements have significant implications for various computer vision applications, leading to improved accuracy and efficiency in image analysis and generation.