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• W3048306538 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 overfitting given scarce training data. We show that this model significantly outperforms a number of existing state-of-the-art models designed for video analysis." @default.
• W3048306538 created "2020-08-13" @default.
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• W3048306538 date "2021-05-01" @default.
• W3048306538 modified "2023-10-16" @default.
• W3048306538 title "Fine-Grained Instance-Level Sketch-Based Video Retrieval" @default.
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• W3048306538 doi "https://doi.org/10.1109/tcsvt.2020.3014491" @default.
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