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The ability to semantically interpret hand-drawn line sketches, although verychallenging, can pave way for novel applications in multimedia. We proposeSketchParse, the first deep-network architecture for fully automatic parsing offreehand object sketches. SketchParse is configured as a two-level fullyconvolutional network. The first level contains shared layers common to allobject categories. The second level contains a number of expert sub-networks.Each expert specializes in parsing sketches from object categories whichcontain structurally similar parts. Effectively, the two-level configurationenables our architecture to scale up efficiently as additional categories areadded. We introduce a router layer which (i) relays sketch features from sharedlayers to the correct expert (ii) eliminates the need to manually specifyobject category during inference. To bypass laborious part-level annotation, wesketchify photos from semantic object-part image datasets and use them fortraining. Our architecture also incorporates object pose prediction as a novelauxiliary task which boosts overall performance while providing supplementaryinformation regarding the sketch. We demonstrate SketchParse's abilities (i) ontwo challenging large-scale sketch datasets (ii) in parsing unseen,semantically related object categories (iii) in improving fine-grainedsketch-based image retrieval. As a novel application, we also outline howSketchParse's output can be used to generate caption-style descriptions forhand-drawn sketches.

SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks