## 4 Experimental Results
### Ablation Studies
When comparing its performance with the final result (model (5) in Table 3), the network without the inner alignment loss achieved much worse results, underscoring the validity of the underlying assumption. **Effectiveness of reconstruction-based alignment method:** In the cross-modal alignment module in Section 3.3, we proposed to combine scoring-based and reconstruction-based methods to explore the complementarity of the two approaches. To verify its effectiveness, we designed a comparison experiment in which we removed the reconstruction loss \(\mathcal{L}_{rec}\) in Equation (9). The performance of models (3) and (5) in Table 3 exhibits a significant disparity, demonstrating the effectiveness of the reconstruction-based alignment method. Theoretically, the reconstruction-based method enables the model to learn semantic consistency in the local fragment of the video, leading to more accurate predictions on temporal boundaries. **Influence of language embedding:** In Section 3.2, we used GloVe to extract the feature representation of each word to represent the language query. Compared to using the feature embedding of the whole language, our method allowed the Transformer to adaptively learn the relationships of different words. To verify this, we conducted ablation studies which applied max pooling and average pooling on the feature representation of each word to generate sentence embeddings. The results are shown in Table 4. Although feature embeddings of the whole sentence contained global semantic information, our method employed Transformer in the fusion stage and was able to learn the cross-modal alignment at the word level. **Influence of video length and query length:** In the feature extraction, to simplify experiments, we conducted single-parameter variations around the parameters set by CPL based on the Transformer base. As shown in Table 5, increasing the query length did not lead to improvements in metrics because the average word length of the database was limited. Continuing to increase it would have introduced additional redundant computations. Increasing the length of the video significantly escalated computational demands, rendering it computationally infeasible. **Influence of \(K\) initial candidate windows:** In the RL-based MMS, we initially set \(K\) candidate windows. It effectively mitigated the bias related to the initial position of the window during the training stage. Table 6 discusses the influence of different values of \(K\) and settings of candidate windows. The runtime of inference is also demonstrated. It can be seen that our method achieved the nest performance when \(K=5\) without seriously sacrificing speed. **Influence of the threshold in the reward function:** The influence of parameters \(thr\) in Equation (13) is shown in Table 7, which records different settings of \(thr\) along with their corresponding experimental results in terms of R@1 and IoU@\(X\), where \(X\in\{0.3,0.5,0.7\}\). We selected the setting that yielded the best results, which was \(thr=0.8\). **Influence of epoch number \(E\) in Equation (6). ** In Section 3.3, we introduced the concept of a dynamic Gaussian mask, allowing us to allocate attentions to different positions within the video. Before \(E\) epochs of training, the model assigns higher attention to the center of candidate windows to explore semantic information of video. After \(E\) epochs, the model shifts the attention to boundaries to accurately determine temporal positions. In this section, we conducted ablation studies on the epoch number \(E\) in Equation (6) to demonstrate the influence of this parameter. As shown in Figure 4a, the method achieved the maximum IoU when \(E\) was set to 10, and we trained the model for a total of 20 epochs. **Influence of multi-policy movement strategy:** In Table 8, we demonstrate ablation studies on different RL movement strategies to verify the effectiveness of the multi-policy movement strategy employed by our method.