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This work designs a dual-backbone feature extraction architecture for food nutrition estimation, and introduces the Adaptive Spatial Feature Fusion (ASFF), which fuse ingredient semantic features with visual features through Softmax weighted interaction to enhance feature fusion.
Convolution Transformer Integration Network (CTNet) is proposed, a hybrid architecture that couples a residual CNN branch for local detail mining with a Transformer branch for global dependency modeling and achieves effectiveness in food recognition.
Federated learning aims to provide security for client data privacy in practical machine learning applications. In principle, a global server aggregates the models produced by local clients to obtain a global model. However, the server is challenged when collaborating with local clients handling non-identically distrib...
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