The sketchy database: learning to retrieve badly drawn bunnies
Patsorn Sangkloy,Nathan Burnell,Cusuh Ham,James Hays +3 more
- Vol. 35, Iss: 4, pp 119
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TLDR
The Sketchy database is presented, the first large-scale collection of sketch-photo pairs and it is shown that the learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification.Abstract:
We present the Sketchy database, the first large-scale collection of sketch-photo pairs. We ask crowd workers to sketch particular photographic objects sampled from 125 categories and acquire 75,471 sketches of 12,500 objects. The Sketchy database gives us fine-grained associations between particular photos and sketches, and we use this to train cross-domain convolutional networks which embed sketches and photographs in a common feature space. We use our database as a benchmark for fine-grained retrieval and show that our learned representation significantly outperforms both hand-crafted features as well as deep features trained for sketch or photo classification. Beyond image retrieval, we believe the Sketchy database opens up new opportunities for sketch and image understanding and synthesis.read more
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Deeper, Broader and Artier Domain Generalization
TL;DR: In this article, a low-rank parameterized CNN model is proposed for domain generalization, which can learn from multiple training domains and extract a domain-agnostic model that can then be applied to an unseen domain.
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Deeper, Broader and Artier Domain Generalization
TL;DR: This paper builds upon the favorable domain shift-robust properties of deep learning methods, and develops a low-rank parameterized CNN model for end-to-end DG learning that outperforms existing DG alternatives.
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A Neural Representation of Sketch Drawings
David Ha,Douglas Eck +1 more
TL;DR: Sketch-rnn is presented, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects that is trained on thousands of crude human-drawn images representing hundreds of classes.
Proceedings ArticleDOI
Scribbler: Controlling Deep Image Synthesis with Sketch and Color
TL;DR: In this paper, the authors proposed a deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces, which allows users to scribble over the sketch to indicate preferred color for objects.
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Scribbler: Controlling Deep Image Synthesis with Sketch and Color
TL;DR: A deep adversarial image synthesis architecture that is conditioned on sketched boundaries and sparse color strokes to generate realistic cars, bedrooms, or faces is proposed and demonstrates a sketch based image synthesis system which allows users to scribble over the sketch to indicate preferred color for objects.
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