SketchEmbedNet: Learning Novel Concepts by Imitating Drawings
By Alexander Wang, Mengye Ren, Richard S. Zemel. 2020
24 citations recorded by the source corpus.
Sketch drawings are an intuitive visual domain that generally preserves semantics. Previous work has shown that recurrent neural networks are capable of producing sketch drawings of a single or few classes at a time. In this work we focus on the representations developed by training a generative model to produce sketches from pixel images across many classes in a sketch domain. We find that the embeddings learned by this sketching model are extremely informative for visual tasks and infer compositional information. We then use them to exceed state-of-the-art performance in unsupervised few-shot classification on the Omniglot and mini-ImageNet benchmarks. We also leverage the generative capacity of our model to produce high quality sketches of novel classes based on just a single example.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
- Aneeshan Sain
- Ayan Kumar Bhunia
- Tao A. Xiang
- Yi-Zhe Song
- Subhadeep Koley
- Viswanatha Reddy Gajjala
- Yongxin Yang
- Rohit Kundu
Citations in the corpus
- Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (2022, background): “…41, 46, 34, 53, 6, 2], sketch has also been employed in a variety of vision understanding tasks, including segmentation [22], video synthesis [28], representation learning [60, 3], object localisation [55], image-inpainting [62], 3D shape retrieval [31], 3D shape modelling [71], among others [64].”