## 5 Experiments
### Quantitative Analysis  
In this subsection, we will present the experiment results to demonstrate that the proposed blocks can enhance the performance of the novel image captioning tasks. First, we chose several of the most representative state-of-the-art methods, which include both regular and transformer-based methods. The comparison methods are briefly described as follows: (1) OVE [7] (2020), in which a low-cost method is proposed to expand word embeddings from a few images of the novel objects; (2) ANOC [34] (2021), which combines object detector and human attention to guide the model to describe novel objects; (3) NOC-REK [36] (2022) retrieves vocabulary from external knowledge and generates captions; (4) VIVO [54] (2021), (5) VinVL [30] (2021), and (6) Oscar [31] (2020), which are the large-scale vision-language transformer model and fine-tune the pre-trained model to adopt downstream tasks; (7) ClipCap [55] (2021) produces a prefix by applying a mapping network over the CLIP visual embedding; (8) SMALLCAP [39] (2023) generates sentences conditioned on the input image and related captions retrieved from a datastore; (9) UpDown [19] (2018) proposes the standard two-layer LSTM structure, in which the \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & **Common** & **Never** & **Row** & **DiffSen** & **B@1** & **B@4** & **M** & **R** & **C** & **S** \\ \hline \multirow{4}{*}{UpDown} & our & \multirow{2}{*}{\
\begin{tabular}{l} our \\ replace \\ \end{tabular} } & our & 1 & \multirow{2}{*}{44} & **71.38** & 13.16 & 22.71 & 47.02 & 73.25 & 48.86 \\  & & & & 69.59 & **15.50** & **23.56** & **48.44** & **73.37** & **48.94** \\ \cline{2-10}  & & our & 3 & \multirow{2}{*}{157} & **75.76** & **15.64** & **24.25** & **49.83** & **87.93** & **50.13** \\  & random & & 4 & & 73.39 & 13.92 & 24.20 & 49.78 & 77.75 & 44.74 \\ \cline{2-10}  & & glove & 5 & \multirow{2}{*}{47} & **74.46** & 9.57 & 23.58 & 49.72 & 85.79 & 46.62 \\  & & replace & 6 & & 73.86 & **12.91** & **23.76** & **50.96** & **87.17** & **46.85** \\ \cline{2-10}  & & glove & 7 & \multirow{2}{*}{107} & **72.44** & 6.73 & **22.37** & **47.43** & **74.14** & 47.26 \\  & & random & 8 & & 69.92 & **6.86** & 22.15 & 47.29 & 73.82 & **48.24** \\ \hline \multirow{4}{*}{XLAN} & our & \multirow{2}{*}{
\begin{tabular}{l} our \\ replace \\ \end{tabular} } & our & 9 & 47 & **73.36** & **16.27** & **25.24** & **51.71** & **79.67** & **52.75** \\  & & replace & 10 & & 71.21 & 11.40 & 24.61 & 51.00 & 73.05 & 51.22 \\ \cline{2-10}  & & our & 11 & & **74.55** & **17.64** & **25.63** & **53.06** & **93.39** & **50.79** \\  & & random & 12 & & 71.14 & 13.93 & 25.10 & 52.13 & 82.42 & 44.21 \\ \cline{2-10}  & & glove & 13 & \multirow{2}{*}{54} & 75.56 & 12.32 & 25.12 & 51.07 & 93.52 & **46.26** \\  & & replace & 14 & & **75.94** & **18.31** & **25.58** & **51.77** & **96.53** & 45.93 \\ \cline{2-10}  & & glove & 15 & \multirow{2}{*}{104} & **74.03** & 11.97 & 24.53 & 50.67 & **80.94** & **47.89** \\  & & random & 16 & & 72.77 & **15.49** & **24.55** & **51.64** & 78.83 & 46.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation analysis of different embedding methods for never-seen selected tags, where Common and Never denote the embedding of words included or not included in the training set, respectively. Our, glove, replace, and random denote the corresponding word embedding obtained by Semi-Fixed Word Embedding, glove, manual synonym replacement, and random initialization, respectively. Bold for the best. attention LSTM encodes the previous words to decide the current attended region features, and the language LSTM generates the next word based on the attended region features and the previous words; (10) XLAN [4] (2020), which proposes an X-Linear attention to model the 2 nd order interactions with both spatial and channel-wise bilinear attention. In those models, UpDown and XLAN are the regular image captioning models without any models designed for the novel objects. Other models contain particular modules for the novel objects.