Improving Class Imbalanced Few-Shot Image Classification via an Edge-Restrained SinDiffusion Method

Class imbalance significantly hampers recognition accuracy in image classification, which is particularly prevalent in special field datasets. Traditional methodologies addressing imbalance, including techniques like oversampling, have shown limited effectiveness. Recently, the advanced generative capabilities of diffusion models garnered attention, with subsequent developments like SinDiffusion enabling new possibilities for rare class sample generation. However, the outputs of these models often significantly deviate from the original targets. Building on this foundation, our approach incorporates edge constraints to ensure newly generated samples not only differ from originals but also retain essential target features. Experiments validate the effectiveness of our method, highlighting the potential to mitigate class imbalance challenges in classification tasks.