Position-aware Location Regression Network for Temporal Video Grounding

TL;DR

This paper introduces PLRN, a novel network that leverages position-aware features to improve temporal video grounding by understanding comprehensive context with reduced computation.

Contribution

The paper proposes a position-aware location regression network that efficiently encodes context for better temporal grounding of video segments using only one semantic phrase.

Findings

PLRN achieves competitive performance with less computation.

It effectively encodes local and global context.

The method improves grounding accuracy over existing approaches.

Abstract

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.