  Inspired by the activity-silent and persistent activity mechanisms in human
visual perception biology, we design a Unified Static and Dynamic Network
(UniSDNet), to learn the semantic association between the video and text/audio
queries in a cross-modal environment for efficient video grounding. For static
modeling, we devise a novel residual structure (ResMLP) to boost the global
comprehensive interaction between the video segments and queries, achieving
more effective semantic enhancement/supplement. For dynamic modeling, we
effectively exploit three characteristics of the persistent activity mechanism
in our network design for a better video context comprehension. Specifically,
we construct a diffusely connected video clip graph on the basis of 2D sparse
temporal masking to reflect the "short-term effect" relationship. We
innovatively consider the temporal distance and relevance as the joint
"auxiliary evidence clues" and design a multi-kernel Temporal Gaussian Filter
to expand the context clue into high-dimensional space, simulating the "complex
visual perception", and then conduct element level filtering convolution
operations on neighbour clip nodes in message passing stage for finally
generating and ranking the candidate proposals. Our UniSDNet is applicable to
both Natural Language Video Grounding (NLVG) and Spoken Language Video
Grounding (SLVG) tasks. Our UniSDNet achieves SOTA performance on three widely
used datasets for NLVG, as well as three datasets for SLVG, e.g., reporting new
records at 38.88% R<EMAIL_ADDRESS>on ActivityNet Captions and 40.26% R<EMAIL_ADDRESS>on
TACoS. To facilitate this field, we collect two new datasets (Charades-STA
Speech and TACoS Speech) for SLVG task. Meanwhile, the inference speed of our
UniSDNet is 1.56$\times$ faster than the strong multi-query benchmark. Code is
available at: https://github.com/xian-sh/UniSDNet.
