Position-aware Location Regression Network for Temporal Video Grounding
This work addresses the challenge of efficiently grounding semantic phrases in video surveillance, offering an incremental improvement over existing methods.
The paper tackles the problem of temporal video grounding by proposing a Position-aware Location Regression Network (PLRN) that uses position-aware features to understand comprehensive contexts from a single semantic phrase, achieving competitive performance with reduced computation time and memory.
The key to successful grounding for video surveillance is to understand a semantic phrase corresponding to important actors and objects. Conventional methods ignore comprehensive contexts for the phrase or require heavy computation for multiple phrases. To understand comprehensive contexts with only one semantic phrase, we propose Position-aware Location Regression Network (PLRN) which exploits position-aware features of a query and a video. Specifically, PLRN first encodes both the video and query using positional information of words and video segments. Then, a semantic phrase feature is extracted from an encoded query with attention. The semantic phrase feature and encoded video are merged and made into a context-aware feature by reflecting local and global contexts. Finally, PLRN predicts start, end, center, and width values of a grounding boundary. Our experiments show that PLRN achieves competitive performance over existing methods with less computation time and memory.