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Code, demos and data for SketchParse (a neural network for sketch segmentation). Paper:

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sketch-parse

This repository contains code and additional material for our paper titled "SketchParse: Towards Rich Descriptions For Poorly Drawn Sketches Using Multi-Task Deep Networks", ACM Multimedia (ACMMM) 2017.

This is a neural network for (semantic) sketch segmentation. Use it to associate semantics with your freehand sketches!

Results

The four panels are chosen from the 100th, 75th, 50th, 25th percentile accuracy of segmentation (by IoU) respectively. As you can see, even in (relatively) bad cases, we can provide fairly accurate segmentations!

Model

We have a multi-task deep neural network that can segment freehand sketches as well as predict a global pose:

Results

Contents

Requirements

This code was developed and tested on an Ubuntu 14.04 machine with python 2.7 and pyTorch (v0.1.12). We used an NVIDIA TITAN X for training and evaluating our model.

If you use this work, please cite the paper:

SketchParse: Towards Rich Descriptions For Poorly Drawn Sketches Using Multi-Task Deep Networks

Questions ?

  • For questions regarding the main segmentation network, please contact Isht ([email protected])

  • For questions regarding the annotation tool, please contact Sahil ([email protected])

  • For questions regarding the pose subnetwork and the sketch-based image retrieval application, please contact Abhijat ([email protected])

  • For any other questions, please contact Ravi ([email protected])