SE-DETR: Explicit Semantic Exploration for Generalizability and Distinguishability in Video Temporal Grounding

Video Temporal Grounding (VTG) aims to localize momentsin untrimmed videos based on language queries. A fundamental chal-lenge lies in learning cross-modal representations that capture bothsemantic correspondence and temporal specificity. Existing approachespredominantly rely on token-level visual-textual cross-attention, whichmay not fully capture two essential properties: generalizability acrossvideos and distinguishability within videos. In practice, semanticallyrelated vocabularies may correspond to similar visual concepts, whiletemporally sparse yet crucial semantics can be easily diluted by fre-quently occurring visual patterns. To address these challenges, we pro-pose Semantic-Explicit Detection Transformer (SE-DETR), a unifiedframework that explicitly models semantic structures for video-languagegrounding. We introduce a Semantics-Proxied Alignment (SPA) mod-ule that learns semantic proxies dynamically optimized during training,and enables concept-level alignment between multimodal representations.Furthermore, we design a Temporal Sparsity Modulation (TSM) mod-ule that estimates the temporal sparsity of semantic components anddynamically reweights word-guided visual features to highlight informa-tive concepts for accurate moment localization. Extensive experimentson five benchmarks demonstrate that SE-DETR consistently achievescompetitive performance across Video Moment Retrieval, Highlight De-tection, and Video Summarization tasks. The source code is avaliable at:https://github.com/hu-cheng-yang/ECCV26-SE-DETR.