The existing image classification methods depend on large-scale manually annotated data.However,when data is limited,these methods suffer from deficiencies in both local feature representation and the number of samples.To address these issues,a method for few-shot image classification based on local contrastive learning and novel class feature generation is proposed.First,local contrastive learning is introduced to represent images as multiple local features and conduct supervised contrastive learning among these local features.Thus,the model capability to represent local features is enhanced.Second,global contrastive learning is employed to ensure the separability of the overall image features.Finally,a feature generation method is proposed to mitigate the data scarcity issue under few-shot conditions.Experiments on public datasets demonstrate the superiority of the proposed method.
维普
万方数据