  Given an untrimmed video and natural language query, video sentence grounding
aims to localize the target temporal moment in the video. Existing methods
mainly tackle this task by matching and aligning semantics of the descriptive
sentence and video segments on a single temporal resolution, while neglecting
the temporal consistency of video content in different resolutions. In this
work, we propose a novel multi-resolution temporal video sentence grounding
network: MRTNet, which consists of a multi-modal feature encoder, a
Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is
an encoder-decoder network, and output features in the decoder part are in
conjunction with Transformers to predict the final start and end timestamps.
Particularly, our MRT module is hot-pluggable, which means it can be seamlessly
incorporated into any anchor-free models. Besides, we utilize a hybrid loss to
supervise cross-modal features in MRT module for more accurate grounding in
three scales: frame-level, clip-level and sequence-level. Extensive experiments
on three prevalent datasets have shown the effectiveness of MRTNet.
