Local-Global Video-Text Interactions for Temporal Grounding
It addresses the problem of accurately localizing video segments based on text queries for applications like video search and analysis, showing incremental improvements over existing methods.
The paper tackles text-to-video temporal grounding by proposing a regression-based model that extracts mid-level features for semantic phrases and reflects bi-modal interactions, achieving state-of-the-art performance with improvements of 7.44% on Charades-STA and 4.61% on ActivityNet Captions at Recall@tIoU=0.5.
This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query. We tackle this problem using a novel regression-based model that learns to extract a collection of mid-level features for semantic phrases in a text query, which corresponds to important semantic entities described in the query (e.g., actors, objects, and actions), and reflect bi-modal interactions between the linguistic features of the query and the visual features of the video in multiple levels. The proposed method effectively predicts the target time interval by exploiting contextual information from local to global during bi-modal interactions. Through in-depth ablation studies, we find out that incorporating both local and global context in video and text interactions is crucial to the accurate grounding. Our experiment shows that the proposed method outperforms the state of the arts on Charades-STA and ActivityNet Captions datasets by large margins, 7.44\% and 4.61\% points at Recall@tIoU=0.5 metric, respectively. Code is available in https://github.com/JonghwanMun/LGI4temporalgrounding.