  Temporal sentence grounding is a challenging task that aims to localize the
moment spans relevant to a language description. Although recent DETR-based
models have achieved notable progress by leveraging multiple learnable moment
queries, they suffer from overlapped and redundant proposals, leading to
inaccurate predictions. We attribute this limitation to the lack of
task-related guidance for the learnable queries to serve a specific mode.
Furthermore, the complex solution space generated by variable and
open-vocabulary language descriptions exacerbates the optimization difficulty,
making it harder for learnable queries to distinguish each other adaptively. To
tackle this limitation, we present a Region-Guided TRansformer (RGTR) for
temporal sentence grounding, which diversifies moment queries to eliminate
overlapped and redundant predictions. Instead of using learnable queries, RGTR
adopts a set of anchor pairs as moment queries to introduce explicit regional
guidance. Each anchor pair takes charge of moment prediction for a specific
temporal region, which reduces the optimization difficulty and ensures the
diversity of the final predictions. In addition, we design an IoU-aware scoring
head to improve proposal quality. Extensive experiments demonstrate the
effectiveness of RGTR, outperforming state-of-the-art methods on QVHighlights,
Charades-STA and TACoS datasets.
