Sketch Recognition
12 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Sketch Recognition
Quick, Draw! Dataset
ShoeV2
AtyPict
Latest papers
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
cai-jianfeng/flowmind2digital
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Automated conversion methods are essential to overcome manual conversion challenges.
Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category.
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
Sketching is a powerful tool for creating abstract images that are sparse but meaningful.
Abstracting Sketches through Simple Primitives
explainableml/sketch-primitives
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Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives under the influence of a budget.
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
avalonstrel/SketchBERT
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CVPR 2020
Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
Multi-Graph Transformer for Free-Hand Sketch Recognition
PengBoXiangShang/multigraph_transformer
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In this work, we propose a new representation of sketches as multiple sparsely connected graphs.
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
erilyth/DistributionAwareBinarizedNetworks-WACV18
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We present a theoretical analysis of the technique to show the effective representational power of the resulting layers, and explore the forms of data they model best.
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
tosmaster/imagevision
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CVPR 2018
Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
In our work, we propose a recurrent neural network architecture for sketch object recognition which exploits the long-term sequential and structural regularities in stroke data in a scalable manner.
