## 5 Experiments
### Qualitative Analysis  
Table 5 shows a few examples of image, caption, and the loss for each word generated by the base models (random) and the model with Semi-Fixed Word Embedding (fixEmbed). We generated the sentences with the same setting, base model, training data, and training strategy. From these examples, we found that although some images can also generate the same sentence through random initialization of the word embedding for novel words, the random initialization increases the loss of words after the novel word. For example, our \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**In-Domain**} & \multicolumn{2}{c}{**Near-Domain**} & \multicolumn{2}{c}{**Out-of-Domain**} & \multicolumn{2}{c}{**Overall**} \\  & **C** & **S** & **C** & **S** & **C** & **S** & **C** & **S** \\ \hline OVE [7] & 79.5 & 11.5 & 74.7 & 11.1 & 78.2 & 10.7 & 76.1 & 11.1 \\ ANOC [34] & 86.1 & 12.0 & 80.7 & 11.9 & 73.7 & 10.1 & 80.1 & 11.6 \\ NOC-REK [36] & 104.7 & 14.8 & 100.2 & 14.1 & 100.7 & 13.0 & 100.9 & 14.0 \\ VIVO [54] & 92.2 & 12.9 & 87.8 & 12.6 & 87.5 & 11.5 & 88.3 & 12.4 \\ VinVL [30] & 96.8 & 13.5 & 90.7 & 13.1 & 87.4 & 11.6 & 90.9 & 12.8 \\ VinVL + VIVO [30; 54] & 103.7 & 13.7 & 95.6 & 13.4 & 83.8 & 11.9 & 94.3 & 13.1 \\ ClipCap [55] & 74.5 & - & 65.6 & - & 47.1 & - & 63.4 & - \\ SMALLCAP [39] & 87.9 & - & 84.6 & - & 84.4 & - & 85.0 & - \\ Oscar * [31] & **107.7** & 14.7 & **102.1** & 14.4 & 99.6 & 13.2 & 102.4 & 14.2 \\ UpDown * [19] & 87.8 & 11.9 & 80.0 & 11.7 & 74.1 & 10.5 & 79.9 & 11.5 \\ XLAN * [4] & 91.6 & 12.9 & 84.3 & 12.4 & 75.0 & 10.8 & 83.5 & 12.2 \\ \hline Oscar+our & 106.2 & **15.0** & 101.8 & **14.6** & **104.7** & 13.6 & **103.0** & **14.5** \\ UpDown+our & 90.2 & 12.6 & 84.9 & 12.1 & 80.7 & 11.1 & 84.8 & 12.0 \\ XLAN+our & 91.4 & 13.3 & 87.6 & 12.9 & 81.4 & 11.9 & 86.9 & 12.7 \\ \hline Human & 84.4 & 14.3 & 85.0 & 14.3 & 95.7 & **14.0** & 87.1 & 14.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of Nocaps online validation. Bold for the best. * indicates our implementations. Bold for the best. proposed block benefits the model more confidently by generating \"in a field\" in the first example, \"in a living room\" in the second example, \"sitting on top of a table\" in the third example, \"with a bunch of fish\" in the fourth example. The fact that the captioning model with Semi-Fixed Word Embedding is able to say subsequent words more accurately means that the proposed block enables the captioning model to better understand the meanings of the never-seen words. Table 6 shows a few examples with images and captions generated by the traditional selection way (base) and our proposed Candidate Sentences Selection (CSS). We generate the captions using the same model and only modify the way to select candidate sentences. Observing the examples, we found that this \"base\" method is difficult to accurately reflect the image content, while our block can result in better captions. More specifically, our proposed block is superior in the following two aspects: (1) The proposed CSS could assist the captioning model in choosing selected tags. For example, our proposed blocks help the model only describe the \"jaguar\" in the first example. (2) The proposed CSS could assist the captioning model in generating sentences that better match the content of the image. For example, there is \"canary\" not \"canary bird\" sitting on top of a chair in the second example, there is \"a swimming pool and palm trees and a umbrella\" not \"a group of chairs and palm trees on a umbrella.\" in the third example, which incorrectly describes the relationships between the palm trees and umbrella. In the fourth example, the base model chooses \"a invertebrates of fish on top of a fish.\", but CSS chooses \"a bunch of fish on top of a invertebrates\". It is clear that although the model does not understand the meaning of \"invertebrates\", the CSS is able to select sentences that better match the meaning of the image.