  Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a
target moment semantically according to a sentence query. Although previous
respectable works have made decent success, they only focus on high-level
visual features extracted from the consecutive decoded frames and fail to
handle the compressed videos for query modelling, suffering from insufficient
representation capability and significant computational complexity during
training and testing. In this paper, we pose a new setting, compressed-domain
TSG, which directly utilizes compressed videos rather than fully-decompressed
frames as the visual input. To handle the raw video bit-stream input, we
propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF)
framework, which extracts and aggregates three kinds of low-level visual
features (I-frame, motion vector and residual features) for effective and
efficient grounding. Particularly, instead of encoding the whole decoded frames
like previous works, we capture the appearance representation by only learning
the I-frame feature to reduce delay or latency. Besides, we explore the motion
information not only by learning the motion vector feature, but also by
exploring the relations of neighboring frames via the residual feature. In this
way, a three-branch spatial-temporal attention layer with an adaptive
motion-appearance fusion module is further designed to extract and aggregate
both appearance and motion information for the final grounding. Experiments on
three challenging datasets shows that our TCSF achieves better performance than
other state-of-the-art methods with lower complexity.
