Existing sketch-analysis work studies sketches depicting static objects or scenes. In this work, we propose a novel cross-modal retrieval problem of fine-grained instance-level sketch-based video retrieval (FG-SBVR), where a sketch sequence is used as a query to retrieve a specific target video instance. Compared with sketch-based still image retrieval, and coarse-grained category-level video retrieval, this is more challenging as both visual appearance and motion need to be simultaneously matched at a fine-grained level. We contribute the first FG-SBVR dataset with rich annotations. We then introduce a novel multi-stream multi-modality deep network to perform FG-SBVR under both strong and weakly supervised settings. The key component of the network is a relation module, designed to prevent model over-fitting given scarce training data. We show that this model significantly outperforms a number of existing state-of-the-art models designed for video analysis.
@article{arxiv.2002.09461,
title = {Fine-Grained Instance-Level Sketch-Based Video Retrieval},
author = {Peng Xu and Kun Liu and Tao Xiang and Timothy M. Hospedales and Zhanyu Ma and Jun Guo and Yi-Zhe Song},
journal= {arXiv preprint arXiv:2002.09461},
year = {2020}
}