As a pivotal area of research in the field of computer vision, the technology for food identification has become indispensable across diverse domains including dietary nutrition monitoring, intelligent service provision in restaurants, and ensuring quality control within the food industry. However, recognizing food images falls within the domain of Fine-Grained Visual Classification (FGVC), which presents challenges such as inter-class similarity, intra-class variability, and the complexity of capturing intricate local features. Researchers have primarily focused on deep information in deep convolutional neural networks for fine-grained visual classification, often neglecting shallow and detailed information. Taking these factors into account, we propose a Multi-level Attention Feature Fusion Network (MAF-Net). Specifically, we use feature maps generated by the Convolutional Neural Networks (CNNs) backbone network at different stages as inputs. We apply a self-attention mechanism to identify local features on these feature maps and then stack them together. The feature vectors obtained through the attention mechanism are then integrated with the original input to enhance data augmentation. Simultaneously, to capture as many local features as possible, we encourage multi-scale features to concentrate on distinct local regions at each stage by maximizing the Kullback-Leibler Divergence (KL-divergence) between the different stages. Additionally, we present a novel approach called subclass center loss (SCloss) to implement label smoothing, minimize intra-class feature distribution differences, and enhance the model’s generalization capability. Experiments conducted on three food image datasets—CETH Food-101, Vireo Food-172, and UEC Food-100—demonstrated the superiority of the proposed model. The model achieved Top-1 accuracies of 90.22%, 89.86%, and 90.61% on CETH Food-101, Vireo Food-172, and UEC Food-100, respectively. Notably, our method not only outperformed other methods in terms of the Top-5 accuracy of Vireo Food-172 but also achieved the highest performance in the Top-1 accuracies of UEC Food-100.

Objective Campylobacter species are the main causes of foodborne illness worldwide, posing significant threats to public health. This study aimed to investigate the antibiotic resistance and genomic characterization of C. jejuni/C.coli from retail chickens in Beijing. Methods Antimicrobial susceptibility testing was conducted on 126 C. jejuni/C. coli isolated from retail chickens in Beijing, following CLSI protocols. Whole genomes of all isolates were sequenced using the Illumina platform. Results More C. coli (83.82%) showed multi-drug resistance than C. jejuni (8.62%). Genomic analysis demonstrated 42 sequence types (STs) and 12 clonal complexes (CCs), from which CC828 and CC52 were dominant. cdtA, cdtB and cdtC encoding cytotoxic protein were present spontaneously in most C. jejuni but not found in any C. coli isolates. The abundances of antibiotic resistance genes (ARGs) and virulence genes (VGs) in C. jejuni and C. coli were significantly different, with ARGs numbered in C. coli and VGs in C. jejuni. Conclusions High prevalence of multi-drug resistance C. coli and C. jejuni isolated from Beijing chickens were challenging clinical antibiotic usages in the treatment of Campylobacter infection. The surveillance of particular C. jejuni and C. coli STs correlated with higher resistance and virulence needs to be strengthened in the future.