Temporal sentence grounding aims to localize moments relevant to a language
description. Recently, DETR-like approaches have shown notable progress by
decoding the center and length of a target moment from learnable queries.
However, they suffer from the issue of center misalignment raised by the
inherent ambiguity of moment centers, leading to inaccurate predictions. To
remedy this problem, we introduce a novel boundary-oriented moment formulation.
In our paradigm, the model no longer needs to find the precise center but
instead suffices to predict any anchor point within the interval, from which
the onset and offset are directly estimated. Based on this idea, we design a
Boundary-Aligned Moment Detection Transformer (BAM-DETR), equipped with a
dual-pathway decoding process. Specifically, it refines the anchor and
boundaries within parallel pathways using global and boundary-focused
attention, respectively. This separate design allows the model to focus on
desirable regions, enabling precise refinement of moment predictions. Further,
we propose a quality-based ranking method, ensuring that proposals with high
localization qualities are prioritized over incomplete ones. Extensive
experiments verify the advantages of our methods, where our model records new
state-of-the-art results on three benchmarks. Code is at
this https URL

我们引入了一种新颖的基于边界的时序定位模型，通过设计 Boundary-Aligned Moment Detection Transformer（BAM-DETR）和质量优先的排序方法，实现了在时序句子理解任务中的新的最佳成果。