Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge Distillation

TL;DR

This paper introduces an efficient multi-teacher knowledge distillation framework for temporal sentence grounding in videos, achieving high accuracy with reduced computational complexity.

Contribution

It proposes a novel EMTM model with a unified feature approach, a knowledge aggregation unit, and a shared encoder strategy to enhance efficiency and performance.

Findings

Outperforms existing methods on three benchmarks.

Reduces computational load while maintaining accuracy.

Effectively integrates diverse teacher knowledge.

Abstract

Temporal Sentence Grounding in Videos (TSGV) aims to detect the event timestamps described by the natural language query from untrimmed videos. This paper discusses the challenge of achieving efficient computation in TSGV models while maintaining high performance. Most existing approaches exquisitely design complex architectures to improve accuracy with extra layers and loss, suffering from inefficiency and heaviness. Although some works have noticed that, they only make an issue of feature fusion layers, which can hardly enjoy the highspeed merit in the whole clunky network. To tackle this problem, we propose a novel efficient multi-teacher model (EMTM) based on knowledge distillation to transfer diverse knowledge from both heterogeneous and isomorphic networks. Specifically, We first unify different outputs of the heterogeneous models into one single form. Next, a Knowledge Aggregation Unit (KAU) is built to acquire high-quality integrated soft labels from multiple teachers. After that, the KAU module leverages the multi-scale video and global query information to adaptively determine the weights of different teachers. A Shared Encoder strategy is then proposed to solve the problem that the student shallow layers hardly benefit from teachers, in which an isomorphic teacher is collaboratively trained with the student to align their hidden states. Extensive experimental results on three popular TSGV benchmarks demonstrate that our method is both effective and efficient without bells and whistles.