  Temporal video grounding (TVG) aims to retrieve the time interval of a
language query from an untrimmed video. A significant challenge in TVG is the
low "Semantic Noise Ratio (SNR)", which results in worse performance with lower
SNR. Prior works have addressed this challenge using sophisticated techniques.
In this paper, we propose a no-frills TVG model that consists of two core
modules, namely multi-scale neighboring attention and zoom-in boundary
detection. The multi-scale neighboring attention restricts each video token to
only aggregate visual contexts from its neighbor, enabling the extraction of
the most distinguishing information with multi-scale feature hierarchies from
high-ratio noises. The zoom-in boundary detection then focuses on local-wise
discrimination of the selected top candidates for fine-grained grounding
adjustment. With an end-to-end training strategy, our model achieves
competitive performance on different TVG benchmarks, while also having the
advantage of faster inference speed and lighter model parameters, thanks to its
lightweight architecture.
