  Temporal Language Grounding seeks to localize video moments that semantically
correspond to a natural language query. Recent advances employ the attention
mechanism to learn the relations between video moments and the text query.
However, naive attention might not be able to appropriately capture such
relations, resulting in ineffective distributions where target video moments
are difficult to separate from the remaining ones. To resolve the issue, we
propose an energy-based model framework to explicitly learn moment-query
distributions. Moreover, we propose DemaFormer, a novel Transformer-based
architecture that utilizes exponential moving average with a learnable damping
factor to effectively encode moment-query inputs. Comprehensive experiments on
four public temporal language grounding datasets showcase the superiority of
our methods over the state-of-the-art baselines.
