# Deep Learning in Food Image Recognition: A Comprehensive Review
## 3 Food Image Recognition Methods
### Dedicated Deep Networks
Figure 7: The illustration for the whole framework. (**a**) The Convolution-Enhanced Bi-Branch Adaptive Transformer (CBiAFormer). (**b–d**) The Structural Learning and Cross-Task Interaction (SLCI). network models. Table S5 summarizes the main models mentioned above, including the dataset evaluated, performance metrics, and key features. The performance of food recognition models proposed in current research often depends on the scale and cultural coverage of the training set, and its performance may degrade when the model is deployed to the real scene or changed to the test scene. For example, in cross-cultural tests, models trained on Western food data show significant performance degradation on Asian food datasets. Although the CBiAFormer model proposed by [PERSON] et al. (2015) achieved an excellent performance of 92.40% Top-1 accuracy on ETHZ Food-101 (Western dish dataset), the Top-1 accuracy on ISIA Food-200 (mixed dish dataset) was only 72.92%. Compared with its performance on Food-101, it is decreased by about 21%. This significant performance difference reveals the generalization limitations of current models in cross-cultural scenarios. Due to the particularity of food images, there is a semantic gap between the general features of natural images (e.g., edges and textures, etc.) and the high-order features specific to food (e.g., food ingredient combination patterns, etc. ), which makes the recognition effect of traditional transfer learning limited, and there is a problem of \"feature mismatch\". For example, in the experiments of [PERSON] et al. (2018), the average accuracy of a single model is only 88.35% when directly using ImageNet pre-trained models such as VGG19, ResNet50, and MobileNetV2 for transfer learning on the FOOD-11 dataset. It is much lower than the accuracy of 96.88% after introducing ensemble learning on the basis of transfer learning. It indicates that more refined feature extraction and domain adaptation methods are needed for food image recognition. Therefore, in order to improve the practical value of the model, it is necessary to construct a more representative multicultural and large-scale food image dataset and develop algorithms that can effectively utilize limited samples. Recently, some studies ([PERSON] et al., 2018) have begun to explore methods such as self-supervised learning to deal with the problem of insufficient data, which will be an important research direction in the future. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{CNN based} & Strong ability to extract local features such as texture and shape & Limited ability to model global semantic relationships \\  & Simple structure, easier to train and deploy & Overfitting can occur on small datasets \\ \hline \multirow{3}{*}{Vision Transformer} & Self-attention mechanisms capture global dependencies & Performance declines on small datasets \\  & Strong robustness to data distribution changes & High computational complexity, \\  & Suitable for large-scale pre-training & performance is more obvious on the high \\ \hline \multirow{3}{*}{Multimodal fusion} & Combine visual and non-visual information & \\  & Strong generalization ability in & High labeling cost \\  & cross-cultural scenes & Multi-source data alignment is complex \\  & Support complex downstream tasks such as & High complexity of model design \\  & nutritional assessment & \\ \hline \multirow{3}{*}{Lightweight neural network models} & Small computational resource requirements & Accuracy may drop for larger models \\  & It has good flexibility and real-time & Need a targeted optimization design \\ \cline{1-1}  & performance in actual deployment & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of deep learning models in food image recognition.