## 2 Related Work  
### Weakly Supervised Temporal Grounding  
In recent years, fully supervised temporal sentence grounding methods [26; 27; 28; 29; 30; 31] have made significant advancements in addressing the primary challenges associated with this task. These challenges include the extraction of contextual information [32; 33] from language queries and spatio-temporal cues from videos, as well as the seamless integration of cross-modal features. However, the heavy burden of collecting temporal moment annotations has driven researchers to increasingly focus on developing weakly supervised learning methods, where only video and query pairs are provided for training. Consequently, there is a critical need for modeling the local alignment [34; 35] between video clips and language. For instance, [PERSON] et al. [10] proposed a two-stage model that approached that task in a coarse-to-fine manner using a sliding-window strategy. [PERSON] [36] emphasized learning fine-grained cross-modal alignment maps to preserve temporal and structural information from video clips while capturing the nuances of complex sentence semantics. [PERSON] [14] placed an emphasis on learning contextualized visual-semantic representations through fine-grained frame-by-word interactions. Meanwhile, [PERSON] [16] introduced a multi-granularity moment candidate generation method and captured temporal dependencies among these candidate moments.  
Existing methods propose several strategies to address the bias problem. For example, they explore down-sampling negative samples [13; 37], mining hard negative samples [38; 39], and reducing the influence of negative samples in the loss function [40; 41]. However, these approaches often result in the loss of valuable information, and the process of identifying the correct proposal from a large pool of samples can be time-consuming and inefficient. Most existing methods, such as those in [15; 42], primarily focus on capturing video-level alignment with queries. They tend to be less effective at modeling detailed consistency between video clips and the query, which limits their ability to accurately predict temporal boundaries. In contrast, [43] employed geodetic distance to address bias problems related to potential connections between video clips. To avoid biased and partial grounding results, a label propagation algorithm [44] was developed to iteratively refine proposal-wise confidence scores.  
The attention mechanism [45; 46] has proven to be an effective strategy for fusing cross-modal features. For example, [PERSON] et al. [47] introduced a video semantic attention module designed to emphasize semantic information in cross-modal fusion. [PERSON] [11] took a different approach by selecting the top-K proposals, considering both exploration and exploitation, and then measuring the semantic similarity between the video and query. Additionally, [12] proposed a multi-directional attention mechanism with query representation. Furthermore, some researchers have focused on contrastive learning methods to distinguish between matched video-textual pairs and unmatched ones. [PERSON] [41] developed a contrastive training approach that involved counterfactual positive and negative results. Meanwhile, RTBPN [40] considered both inter-sample and intra-sample confrontations through a shared two-branch framework.