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arXiv · 2002.09461

Fine-Grained Instance-Level Sketch-Based Video Retrieval

Peng Xu·Kun Liu·Tao Xiang·Timothy M. Hospedales·Zhanyu Ma·Jun Guo·Yi-Zhe Song
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Abstract

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.

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Peng Xu, Kun Liu, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo, Yi-Zhe Song. 2020-02-21. Fine-Grained Instance-Level Sketch-Based Video Retrieval. https://arxiv.org/abs/2002.09461

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