# Image Captioning with Word Gate and Adaptive Self-Critical Learning
## 4 Experiments
### Results  
We used CIDEr [5], BLEU [22], METEOR [23], and ROUGE_L metrics to evaluate the quality of the generated sentences. In practice, we found that the reward obtained from the sample and the reward obtained from the greedy method were very different. This made the training loss become large, as shown in Figure 2. For comparison, we used both SCST and ASCST to fine-tune the same WG model. In Figure 2, the scores were evaluated every 50 iterations under the MSCOCO testing dataset. They were both trained with the same initial model, whose CIDEr was 1.092. From Figure 2, we can find that the ASCST model had better performance than the original SCST method. At the beginning of the RL, the SCST method obtained a lower result than the initial model, but ASCST was not affected by the difference of the two rewards, and it could improve the CIDEr score continuously. In Table 1, we report the performance of the soft attention (Resnext_101_64x4d) baseline model [10], then we add the proposed word gate model (WG) to the baseline model without RL. Finally, we add SCST and ASCST to the soft attention + WG model for the comparison between the SCST method and the ASCST method. The above models were all validated on the test portion of the Karpathy splits, and they were all single models without ensemble method. From Table 1, we can find that the soft attention with WG model yielded a better performance than the soft attention model alone. Furthermore, to compare the SCST and the ASCST, we fine-tuned the same soft attention + WG model with the SCST method and ASCST method. Then, we obtained the soft attention + WG + SCST model and the soft attention + WG + ASCST model. From the results, we could find that the soft attention + WG + ASCST model had better performance than the soft attention + WG + SCST model reported in [4] on BLEU, ROUGE-L, and CIDEr metrics. This result shows that the proposed WG method could help the model to obtain better performance. Moreover, the ASCST could not only make the IRL training more stable, but could also improve the model's performance. Compared with other state-of-the-art methods, our model also achieved competitive results. We then used our best model to get the test results with the official test split, and submitted our results to the official MSCOCO evaluation server. In Table 2, the state-of-the-art results on the leaderboard are also depicted. We outperformed the baseline method on all evaluation metrics. To further evaluate the ASCST method and get the appropriate hyper-parameters settings, we tried several comparison experiments where only \(h\) or \(\beta\) in Equation (16) were different. In Figures 5 and 6, we present the model with different \(h\) and \(\beta\) settings. In Figure 5, we can find that the alpha_5000_beta_08 model had the best CIDEr score among these models ( alpha_5000 means the WG model trained with the ASCST method with \(h\) set to be 5000 iterations). From Figure 6, we can see that the performance of our model was significantly influenced by the \(\beta\). Figure 6 shows the influence of the hyper-parameter \(h\). alpha_h_beta_08 refers to the WG model trained with the ASCST method with \(\beta\) set to 0.8, and the \(h\) represent different hyper-parameters for the history factor. We found that with longer history reward information, better performance was obtained. In practice, \(\beta\) set to be 0.8 and \(h\) set to be more than 2500 iterations could help our model to yield better performance.