# Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping
## 4 Results and Analysis
### Comparision  
We first compare our BFM model with some classical and state-of-the-art methods under the CZSL setting. The experimental results on different datasets under the CZSL setting are provided in Table 2. The BFM model proposed in this paper achieves optimal and sub-optimal accuracies of 71.9% and 62.8% on the CUB and SUN datasets, respectively, which indicates that the model extracts the intrinsic representational information that is effective in discriminating fine-grained image samples. Meanwhile, although the model does not achieve the optimal classification results on the AWA2 dataset, the top-1 accuracy is only 69.3. This is because AWA2 is a coarse-grained dataset, which covers a wider range of knowledge, and our model is not good enough to learn them all in the limited information, so the model does not achieve the optimal results on the AWA2 dataset. However, compared with the best results, the gap is also within 3%, which can show that the model is competitive to the existing models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Attributes** & \begin{tabular}{c} **Seen** \\ **Classes** \\ \end{tabular} & \begin{tabular}{c} **Unseen** \\ **Classes** \\ \end{tabular} & \
\begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Test Samples** \\ \end{tabular} \\ \hline Caltech-UCSD-Birds (CUB) & 312 & 150 & 50 & 7057 & 4731 \\ SUN Attribute (SUN) & 102 & 645 & 72 & 10,320 & 4020 \\ Animals with Attributes2 & 85 & 40 & 10 & 23,527 & 13,795 \\ (AWA2) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information statistics of the datasets. The experimental results on different datasets under the GZSL setting are provided in Table 3. The BFM model proposed in this paper has the best classification results on the CUB dataset, with a reconciled mean of 61.6%. The results on the AWA2 dataset are also closer to those of the SOTA method, reaching 66.6%. However, BFM is less effective on the SUN dataset because the SUN dataset has many categories and fewer samples per category, and because it is a scenario class dataset, it is more difficult for the embedded-based model to extract the effective information. The results in the table also show that generative based models are more advantageous on the SUN dataset. In addition, for the evaluation index, i.e., the classification accuracy of seen class samples, it can be seen that many models have achieved very good results on this index, but many models are less effective on the index, i.e., the model's classification accuracy of unseen class samples is worse, which indicates that some models do not compensate much for the seen class favoritism problem, resulting in the model's overfitting to the seen class. While the BFM model proposed in this paper is not as effective as other models in some indicators, the BFM model is more effective in addressing the problem of seen class favoritism, which effectively alleviates the seen class favoritism problem. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Models**} & **CUB** & **SUN** & **AWA2** \\ \cline{3-5}  & & _acc (\%)_ & _acc (\%)_ & _acc (\%)_ \\ \hline \multirow{5}{*}{Generative Methods} & f-CLSWGAN & 57.3 & 60.8 & 68.2 \\  & f-VAEGAN-D2 & 61.0 & **64.7** & 71.1 \\  & Composer & 69.4 & 62.6 & 71.5 \\  & cycle- & 58.4 & 60.0 & 66.3 \\  & CLSWGAN & 58.8 & 61.7 & — \\  & LsGAN & 59.5 & 61.5 & 71.2 \\  & DAZLE & 66.0 & 59.4 & 67.9 \\  & LFGAA & 67.6 & 61.5 & 68.1 \\  & SGMA & 71.0 & — & 68.8 \\  & DSAN & 57.4 & 62.4 & **72.3** \\  & BFM (ours) & **71.9** & 62.8 & 69.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of image classification models under traditional zero-shot learning setting. Note that in the table, bold fonts indicate the best performance in this metric, and underlined fonts indicate the second best performance in this metric. ”–” indicates that the method corresponds to the results not given in the literature.