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
Most implemented papers
Sketch-a-Net that Beats Humans
dasayan05/sketchanet-quickdraw
•
•
We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans.
From Paper to Machine: Extracting Strokes from Images for use in Sketch Recognition
Online sketches provide significantly more information than paper sketches, but they still do not provide the flexibility, naturalness, and simplicity of a simple piece of paper.
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.
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
tosmaster/imagevision
•
•
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.
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
erilyth/DistributionAwareBinarizedNetworks-WACV18
•
•
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.
Multi-Graph Transformer for Free-Hand Sketch Recognition
PengBoXiangShang/multigraph_transformer
•
•
In this work, we propose a new representation of sketches as multiple sparsely connected graphs.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
avalonstrel/SketchBERT
•
•
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.
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.
Abstracting Sketches through Simple Primitives
explainableml/sketch-primitives
•
•
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.
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.
