## 5 Experiments
### Ablative Analysis
#### 5.4.2 The Effectiveness of Proposed Embedding Block for Never-Seen Objects  
Consider that out-of-domain images contain rarely seen and never-seen objects, while the Semi-Fixed Word Embedding focuses on never-seen objects. To further demonstrate the effectiveness of the proposed block, Table 3 shows the scores of sentences generated by \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & **Row** & **In-Domain** & **Near-Domain** & **Out-of-Domain** & & & & **Overall** & & \\ \hline
**Model** & **CBS** & **F** & **G** & **S** & & **C** & **S** & **C** & **S** & **C** & **S** & **B1** & **B4** & **M** & **R** & **C** & **S** \\ \hline \multirow{4}{*}{UpDown} & & & & & 1 & 90.05 & 12.01 & 74.38 & 10.99 & 52.98 & 9.54 & 76.96 & 20.05 & 24.38 & 53.08 & 72.29 & 10.87 \\  & & ✓ & & & 2 & 88.31 & 11.88 & 74.47 & 11.01 & 55.02 & 9.49 & 77.13 & 19.44 & 24.32 & 53.09 & 72.51 & 10.85 \\  & & ✓ & & ✓ & 3 & 92.60 & 12.51 & 79.60 & 11.49 & 62.61 & 10.06 & 78.26 & 20.75 & 25.19 & 53.81 & 78.02 & 11.37 \\  & ✓ & & & & 4 & 87.77 & 11.85 & 80.00 & 11.71 & 74.06 & 10.50 & 78.15 & 18.87 & 24.98 & 53.02 & 79.91 & 11.49 \\  & ✓ & & & ✓ & 5 & **92.70** & 12.49 & 84.49 & 12.05 & 79.34 & **11.19** & 79.28 & 20.19 & 25.81 & 53.39 & 84.63 & 11.95 \\  & ✓ & ✓ & & & 6 & 86.14 & 11.85 & 78.63 & 11.39 & 72.73 & 10.22 & 78.35 & 18.58 & 24.53 & 53.14 & 78.51 & 11.23 \\  & ✓ & ✓ & & & 7 & 86.93 & 11.84 & 79.83 & 11.64 & 73.54 & 10.56 & 78.32 & 18.64 & 24.84 & 53.06 & 79.57 & 11.45 \\  & ✓ & & ✓ & ✓ & 8 & 91.00 & 12.12 & 84.39 & 11.89 & **80.96** & 10.76 & **79.92** & 20.11 & 25.48 & **54.24** & 84.65 & 11.70 \\  & ✓ & ✓ & & ✓ & 9 & 90.16 & **12.55** & **84.87** & **12.08** & 80.66 & 11.07 & 79.54 & **20.63** & **25.89** & 54.12 & **84.78** & **11.95** \\ \hline \multirow{4}{*}{XLAN} & ✓ & & & & 10 & 91.56 & 12.87 & 84.30 & 12.42 & 75.03 & 10.79 & 79.12 & 20.63 & 25.96 & 54.46 & 83.47 & 12.17 \\  & ✓ & & & ✓ & 11 & **93.90** & **13.59** & 87.42 & 12.82 & 80.91 & 11.58 & 80.15 & **21.85** & 26.66 & 55.10 & **87.03** & 12.69 \\  & ✓ & & ✓ & & 12 & 89.98 & 12.71 & 82.95 & 12.40 & 74.97 & 10.86 & 79.10 & 20.57 & 25.78 & 54.10 & 82.34 & 12.14 \\  & ✓ & ✓ & & & 13 & 90.41 & 12.58 & 83.87 & 12.42 & 75.95 & 11.06 & 79.15 & 21.34 & 26.07 & 54.52 & 83.21 & 12.17 \\  & ✓ & & ✓ & & 14 & 93.21 & 13.21 & 86.48 & **12.87** & 79.22 & 11.41 & 80.03 & 21.62 & 26.47 & 54.91 & 85.98 & 12.63 \\  & ✓ & ✓ & & & ✓ & 15 & 91.44 & 13.34 & **87.56** & 12.86 & **81.39** & **11.86** & **80.30** & 21.70 & **26.72** & **55.13** & 86.87 & **12.73** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation analysis for the proposed block on two base models. The F, G, and S denote Semi-Fixed Word Embedding, take glove as word embedding, and Candidate Sentences Selection, respectively. Bold for the best. ✓ for attach the corresponding module. various embedding methods for the never-seen selected tags. In this table, we only analyze the results for different sentences, ignoring the same generated sentences, in which the Common and Never denote the words included or not included in the training set. The our, glove, replace, and random denote the different ways to obtain word embedding. From the result, we can see that the improvement of the CIDEr/SPICE in rows 1 and 2, rows 9 and 10, rows 3 and 4, and rows 11 and 12 is \(-0.12/0.08\), \(6.62/1.53\), \(10.18/5.39\), and \(10.97/6.58\). Compared with random embedding, Semi-Fixed Word Embedding significantly improves the accuracy of generated sentences. Compared with manual replacement, Semi-Fixed Word Embedding performs well on XLAN and slightly worse on UPDN; meanwhile, Semi-Fixed Word Embedding does not need manual replacement. By observing the corresponding results of glove, it is better than random embedding, but worse than manual replacement and Semi-Fixed Word Embedding in UPDN and XLAN, indicating that glove is difficult to reflect the visual semantic of the noun. It is also demonstrated that the proposed block is beneficial for the captioning model to understand the meaning of the noun.