  Temporal sentence grounding aims to localize moments relevant to a language
description. Recently, DETR-like approaches achieved notable progress by
predicting the center and length of a target moment. 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 propose
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 boundaries are directly
estimated. Based on this idea, we design a boundary-aligned moment detection
transformer, 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. Experiments on three benchmarks validate the effectiveness of the
proposed methods. The code is available at
https://github.com/Pilhyeon/BAM-DETR.
