Static and Dynamic Graph Alignment Network for Temporal Video Grounding
Temporal Video Grounding (TVG) aims to localize temporal moments in an untrimmed video that semantically correspond to given natural language queries. Recently, Graph Convolutional Networks (GCN) have been widely adopted in TVG to model temporal relations among video clips and enhance contextual reasoning by constructing clip-level graphs. Despite their effectiveness, existing GCN-based TVG methods encounter three critical bottlenecks: 1) Most methods construct graph nodes using either static or dynamic features alone, resulting in incomplete visual representation and overlooking complementary semantics, 2) Most methods construct temporal graphs in a query-agnostic manner, leading to inef...
| Authors | Zhanjie Hu, Bolin Zhang, Jianhua Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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