  This paper explores the task of Temporal Video Grounding (TVG) where, given
an untrimmed video and a natural language sentence query, the goal is to
recognize and determine temporal boundaries of action instances in the video
described by the query. Recent works tackled this task by improving query
inputs with large pre-trained language models (PLM) at the cost of more
expensive training. However, the effects of this integration are unclear, as
these works also propose improvements in the visual inputs. Therefore, this
paper studies the effects of PLMs in TVG and assesses the applicability of
parameter-efficient training with NLP adapters. We couple popular PLMs with a
selection of existing approaches and test different adapters to reduce the
impact of the additional parameters. Our results on three challenging datasets
show that, without changing the visual inputs, TVG models greatly benefited
from the PLM integration and fine-tuning, stressing the importance of sentence
query representation in this task. Furthermore, NLP adapters were an effective
alternative to full fine-tuning, even though they were not tailored to our
task, allowing PLM integration in larger TVG models and delivering results
comparable to SOTA models. Finally, our results shed light on which adapters
work best in different scenarios.
