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

MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding