Food multi-factor decoupling recognition based on progressive spatial-frequency attention distillation learning

With the widespread application of image recognition technology in daily life, food image recognition faces challenges such as diverse categories and complex forms. Particularly when dealing with subtle differences between similar food items, imbalanced categories, feature ambiguities, and classification confusion caused by the coupling of multiple factors in food representation, existing models still have room for improvement in their recognition accuracy and generalization ability. Therefore, constructing a recognition model that can precisely differentiate food categories while effectively addressing the complexities of coupled factors has become a key issue in this field. In response to these challenges, we propose the innovative Progressive Spatial-Frequency Distillation Network (PSFDNet). By utilizing a unique multidimensional progressive learning strategy combined with an adaptive spatial-frequency attention mechanism, the model significantly enhances its feature extraction and discrimination capabilities within complex food structures. Additionally, we introduce the food correlation evaluation loss to decouple the mutual interference among food features effectively, thereby improving the accuracy and robustness of food image recognition. Extensive experiments verified the outstanding performance of PSFDNet across datasets, demonstrating a notable increase of 0.87% in the Top-1 recognition accuracy and a 50% increase in inference speed. Particularly in recognizing food images characterized by highly coupled features and extremely imbalanced categories, PSFDNet exhibited significant performance advantages over other methods.