Video temporal grounding (VTG) aims to localize relevant moments and predict saliency scores in untrimmed videos based on natural language queries. Recently, DETR-inspired methods have achieved instance-level moment predictions through query-based semantic evidence aggregation. However, unlike spatial objects with clear physical edges, video moments often exhibit ambiguous temporal boundaries due to the semantic gradient. Consequently, relying solely on global semantics can lead to severe boundary misalignment, yielding sub-optimal grounding results.

To address this issue, we propose a novel Dual Evidence Detection Transformer (DE²TR) framework for better boundary localization. In particular, we introduce a dual-branch decoder that simultaneously captures global semantic evidence and fine-grained boundary cues. To prevent feature conflict between these distinct objectives, we channel-wise decouple the moment queries, facilitating independent evidence learning under the supervision of evidence alignment losses. Building upon this dual-branch design, we introduce a prior-guided refinement mechanism that employs explicit prior maps to steer queries toward potential regions of interest. Furthermore, a boundary augmentation strategy synthesizes ideal boundary patterns via simple temporal splicing, effectively enhancing the model’s boundary awareness. Extensive experiments on multiple popular benchmarks demonstrate that DE²TR significantly outperforms state-of-the-art approaches.