  Temporal grounding of activities, the identification of specific time
intervals of actions within a larger event context, is a critical task in video
understanding. Recent advancements in multimodal large language models (LLMs)
offer new opportunities for enhancing temporal reasoning capabilities. In this
paper, we evaluate the effectiveness of combining image-based and text-based
large language models (LLMs) in a two-stage approach for temporal activity
localization. We demonstrate that our method outperforms existing video-based
LLMs. Furthermore, we explore the impact of instruction-tuning on a smaller
multimodal LLM, showing that refining its ability to process action queries
leads to more expressive and informative outputs, thereby enhancing its
performance in identifying specific time intervals of activities. Our
experimental results on the Charades-STA dataset highlight the potential of
this approach in advancing the field of temporal activity localization and
video understanding.
