# MFEM: Multi-View Feature Enhanced Attention Model for Image Captioning
## 4 Experiments
### Ablation Study  
As presented in Table 2, different combinations of the enhancement module are evaluated to comprehensively demonstrate the impact of the multi-view feature enhanced attention. As illustrated in Table 3, Experiments are performed to validate the efficacy of the model averaging. Furthermore, as shown in Table 4, comprehensive ablation experiments are conducted to assess the impact of the Mean Teacher architecture. **Role of the multi-view feature enhanced attention. ** Table 2 presents the experimental results of the enhancement modules, where the models employing individual enhancement modules, namely the self-enhanced Module (SEM) and the sliding window module (SWM), are compared with the proposed MFEAM model. As can be seen, MFEAM achieves competitive performance compared to models that process entangled features from either the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **B-1** & **B-4** & **M** & **R** & **C** & **S** \\ \hline Up-Down [4] & 79.8 & 36.3 & 27.7 & 56.9 & 120.1 & 21.4 \\ GCN-LSTM [9] & 80.5 & 38.3 & 28.5 & 58.3 & 127.6 & 22.0 \\ AoANet [51] & 80.2 & 38.9 & 29.2 & 58.8 & 129.8 & 22.4 \\ EURAIC [56] & 80.9 & 39.5 & 29.4 & 59.4 & 130.3 & - \\ HIP [49] & - & 39.1 & 28.9 & 59.2 & 130.6 & 22.3 \\ VAT [29] & 81.2 & 39.0 & 29.3 & 59.4 & 131.8 & 22.8 \\ X-Transformer [52] & 80.9 & 39.7 & 29.5 & 59.1 & 132.8 & 23.4 \\ DLCT [53] & 81.4 & 39.8 & 29.5 & 59.1 & 133.8 & 23.0 \\ RSTNet [54] & 81.8 & 40.1 & 29.8 & 59.5 & 135.6 & 23.3 \\ DIFNet [50] & 81.7 & 40.0 & 29.7 & 59.4 & 136.2 & 23.2 \\ BENet [55] & 82.1 & 40.3 & 30.0 & 59.7 & 137.6 & 23.6 \\ MFEAM & **82.8** & **40.8** & **30.5** & **60.2** & **140.1** & **24.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of MFEAM on the MSCOCO dataset. Figure 4: Comparison of the computational efficiency of different image captioning methods on MSCOCO dataset. visual or textual view alone. The evaluation metrics of models using only the self-enhanced module or the sliding window module indicate that relying on a single enhancement module leads the teacher model to focus solely on either the visual or textual modality, which limits its ability to capture rich and comprehensive features. In contrast, MFEAM offers a more diverse mechanism for processing image features, enabling a more precise representation of entangled features of vision and text from both views. **Role of the model averaging. ** Table 3 presents the results obtained without applying the model averaging operation. In this setting, knowledge is transferred from the teacher model to the student model by optimizing a distillation-based objective that encourages the student to mimic the output behavior of the teacher. Meanwhile, instead of relying solely on static supervision, the teacher model dynamically incorporates guidance from the student model by tracking and utilizing the exponential moving average of the student's parameters, thereby promoting a mutually reinforcing optimization process. This collaborative interaction the teacher model to provide a smoother and more stable learning target for the student model, thereby effectively reducing uncertainty during training. The performance of MFEAM which uses CLIP-RN50\(\times\)16 and model averaging significantly surpasses that of the approach using CLIP-RN50\(\times\)16 without model averaging. Model averaging enables the teacher model to retain a temporally weighted accumulation of the student model's evolving parameters. This mechanism effectively serves as a form of model ensembling, enhancing the generalization capacity of the teacher model. **Role of the Mean Teacher architecture. ** We evaluate the performance of MFEAM using several image feature extraction modules, as indicated in Table 4. For models without the MFEAM architecture, a single language model is designated as the language decoder. The integration of all previously discussed components within the MFEAM framework leads to a substantial performance improvement, highlighting the effectiveness of the architecture.