One Sketch for All: One-Shot Personalized Sketch Segmentation

• URL: http://arxiv.org/abs/2112.10838v1
• Date: Mon, 20 Dec 2021 20:10:44 GMT
• Title: One Sketch for All: One-Shot Personalized Sketch Segmentation
• Authors: Anran Qi, Yulia Gryaditskaya, Tao Xiang, Yi-Zhe Song
• Abstract summary: We present the first one-shot personalized sketch segmentation method. We aim to segment all sketches belonging to the same category with a single sketch with a given part annotation. We preserve the parts semantics embedded in the exemplar, and we are robust to input style and abstraction.
• Score: 84.45203849671003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the first one-shot personalized sketch segmentation method. We aim to segment all sketches belonging to the same category provisioned with a single sketch with a given part annotation while (i) preserving the parts semantics embedded in the exemplar, and (ii) being robust to input style and abstraction. We refer to this scenario as personalized. With that, we importantly enable a much-desired personalization capability for downstream fine-grained sketch analysis tasks. To train a robust segmentation module, we deform the exemplar sketch to each of the available sketches of the same category. Our method generalizes to sketches not observed during training. Our central contribution is a sketch-specific hierarchical deformation network. Given a multi-level sketch-strokes encoding obtained via a graph convolutional network, our method estimates rigid-body transformation from the reference to the exemplar, on the upper level. Finer deformation from the exemplar to the globally warped reference sketch is further obtained through stroke-wise deformations, on the lower level. Both levels of deformation are guided by mean squared distances between the keypoints learned without supervision, ensuring that the stroke semantics are preserved. We evaluate our method against the state-of-the-art segmentation and perceptual grouping baselines re-purposed for the one-shot setting and against two few-shot 3D shape segmentation methods. We show that our method outperforms all the alternatives by more than 10% on average. Ablation studies further demonstrate that our method is robust to personalization: changes in input part semantics and style differences.

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