# Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification
## 5 Discussion  
FGVC is becoming an increasingly significant computer vision task that has the potential to be applied in numerous scenarios. Human beings have the capability to quickly learn and accurately recognize fine-grained objects in different subcategories, because we can identify subtle distinguishable patterns in the course of learning. This intuition drives a wide spectrum of studies in the deep learning community. Deep CNNs enable automated feature extraction, while attention mechanisms allow CNNs to learn what and where to focus on, improving the quality of extracted features. Specific to FGVC, prior efforts have explored how to effectively represent fine-grained features through ways such as attentive feature learning, local feature map aggregation, multiscale feature extraction, and pairwise feature interaction. Our work also focuses on this core mission. The proposed AIF-Net aims to generate high-quality fine-grained features by fusing attentive local and global features and interactive feature learning. The novel neural structure is verified through extensive experiments and demonstrates consistent performance improvement in comparison with the SOTA. We also discover that a concatenation fusion operation applied in the feature extractor, along with three top-scoring regions suggested by an RPN, can effectively boost the performance. Domain-specific FGVC models can be used to build recognition systems that fit various industrial needs. The proposed AIF-Net model can serve as a fine-grained vegetable and fruit classifier to automate applications in domestic cooking and food management. One interesting use case would be building a vegetable/fruit recognition software for educational or training purposes. To become a food/cooking/nutrition professional, one needs to be trained to recognize fine-grained vegetable/fruit subcategories. Such software can be used to generate exercises of different difficulty levels, asking trainees to distinguish vegetable/fruit types. A more intelligent classifier can even evolve with better recognition skills from food experts through active or reinforcement learning, and on the other hand, convey this novel knowledge to trainees or students, creating an efficient learning loop. This work has the following limitations that also point out our future research directions. First, the biattention module applied in the attentive feature extractor only employs a single MLP to learn the attentions, while a multichannel and multispatial attention mod \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Set** & **Method** & **Top-1 Acc** & **Top-5 Acc** \\ \hline \multirow{6}{*}{Veg200} & ResNet50 & 88.195\% & **98.187\%** \\  & VGG16 & 78.50\% & - \\  & CBP & 81.59\% & - \\  & DCL & 85.98\% & 97.53\% \\  & API-Net & 86.953\% & 97.210\% \\  & WS-DAN & 84.17\% & 96.71\% \\  & AIF-Net & **89.154\%** & 98.045\% \\ \hline \multirow{6}{*}{Fru92} & ResNet50 & 89.323\% & 98.710\% \\  & VGG16 & 79.80\% & - \\  & DCL & 85.07\% & 96.17\% \\  & API-Net & 89.914\% & 98.021\% \\  & WS-DAN & 87.32\% & 98.21\% \\  & AIF-Net & **91.058\%** & **98.809\%** \\ \hline \multirow{6}{*}{VegFru292} & ResNet50 & 88.002\% & 97.992\% \\  & VGG16 & 77.12\% & - \\ \cline{1-1}  & CBP & 82.21\% & - \\ \cline{1-1}  & HybridNet & 83.51\% & - \\ \cline{1-1}  & DCL & 87.13\% & 97.26\% \\ \cline{1-1}  & API-Net & 87.241\% & 97.711\% \\ \cline{1-1}  & WS-DAN & 85.72\% & 97.17\% \\ \cline{1-1}  & AIF-Net & **90.832\%** & **98.619\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A performance comparison of all models. The highest score of each metric is marked in bold-face. ule [32] can be adopted for further improvement. Second, the attentive feature extractor only considers one image/feature scale, while a multiscale-based feature pyramid can be utilized to encourage the network to extract features in multiple granularities. Third, our study does not take advantage of data augmentation, which could be another performance booster.