Efficient Temporal Sentence Grounding in Videos with Multi-Teacher
  Knowledge Distillation
Renjie Liang Yiming Yang Hui Lu Li Li
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.