[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Overview

Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, CVPR 2021.

Ayan Kumar Bhunia, Pinaki nath Chowdhury, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song, “Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

Abstract

Self-supervised learning has gained prominence due to its efficacy at learning powerful representations from unlabelled data that achieve excellent performance on many challenging downstream tasks. However, supervision-free pre-text tasks are challenging to design and usually modality specific. Although there is a rich literature of self-supervised methods for either spatial (such as images) or temporal data (sound or text) modalities, a common pre-text task that benefits both modalities is largely missing. In this paper, we are interested in defining a self-supervised pre-text task for sketches and handwriting data. This data is uniquely characterised by its existence in dual modalities of rasterized images and vector coordinate sequences. We address and exploit this dual representation by proposing two novel cross-modal translation pre-text tasks for self-supervised feature learning: Vectorization and Rasterization. Vectorization learns to map image space to vector coordinates and rasterization maps vector coordinates to image space. We show that our learned encoder modules benefit both raster-based and vector-based downstream approaches to analysing hand-drawn data. Empirical evidence shows that our novel pre-text tasks surpass existing single and multi-modal self-supervision methods.

Outline

Outline

Figure: Schematic of our proposed self-supervised method for sketches. Vectorization drives representation learning for sketch images; rasterization is the pre-text task for sketch vectors.

Architecture

Framework Figure: Illustration of the architecture used for our self-supervised task for sketches and handwritten data (a,c), and how it can subsequently be adopted for downstream tasks (b,d). Vectorization involves translating sketch image to sketch vector (a), and the convolutional encoder used in the vectorization process acts as a feature extractor over sketch images for downstream tasks (b). On the other side, rasterization converts sketch vector to sketch image (c), and provides an encoding for vector-based recognition tasks downstream (d).

Citation

If you find this article useful in your research, please consider citing:

@InProceedings{sketch2vec,
author = {Ayan Kumar Bhunia and Pinaki Nath Chowdhury and Yongxin Yang and Timothy Hospedales and Tao Xiang and Yi-Zhe Song},
title = {Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}

** More polished code is coming **

Work done at SketchX Lab, CVSSP, University of Surrey.

Owner
Ayan Kumar Bhunia
I am a PhD student, focussing on Computer Vision and Deep Learning, at Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey.
Ayan Kumar Bhunia
A Human-in-the-Loop workflow for creating HD images from text

A Human-in-the-Loop? workflow for creating HD images from text DALL·E Flow is an interactive workflow for generating high-definition images from text

Jina AI 2.5k Jan 02, 2023
This repo implements a 3D segmentation task for an airport baggage dataset.

3D CT Scan Segmentation With Occupancy Network This repo implements a 3D superresolution segmentation task for an airport baggage dataset. Our final p

Christoph Reich 2 Mar 28, 2022
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers (arXiv2021)

Polyp-PVT by Bo Dong, Wenhai Wang, Deng-Ping Fan, Jinpeng Li, Huazhu Fu, & Ling Shao. This repo is the official implementation of "Polyp-PVT: Polyp Se

Deng-Ping Fan 102 Jan 05, 2023
Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

Computational Optimal Transport for Machine Learning Reading Group Over the last few years, optimal transport (OT) has quickly become a central topic

Ali Harakeh 11 Aug 26, 2022
Long Expressive Memory (LEM)

Long Expressive Memory for Sequence Modeling This repository contains the implementation to reproduce the numerical experiments of the paper Long Expr

Konstantin Rusch 47 Dec 17, 2022
Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation

CorrNet This project provides the code and results for 'Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation'

Gongyang Li 13 Nov 03, 2022
Repository for open research on optimizers.

Open Optimizers Repository for open research on optimizers. This is a test in sharing research/exploration as it happens. If you use anything from thi

Ariel Ekgren 6 Jun 24, 2022
Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

Reviatalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation This is the implementation of the approach describ

Taosha Fan 47 Nov 15, 2022
Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques Installation PyPI pip install colossalai Install

HPC-AI Tech 7.1k Jan 03, 2023
PyTorch implementation of NeurIPS 2021 paper: "CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration"

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud Registration (NeurIPS 2021) PyTorch implementation of the paper: CoFiNet: Reli

76 Jan 03, 2023
A deep learning framework for historical document image analysis

DIVA-DAF Description A deep learning framework for historical document image analysis. How to run Install dependencies # clone project git clone https

9 Aug 04, 2022
An unopinionated replacement for PyTorch's Dataset and ImageFolder, that handles Tar archives

Simple Tar Dataset An unopinionated replacement for PyTorch's Dataset and ImageFolder classes, for datasets stored as uncompressed Tar archives. Just

Joao Henriques 47 Dec 20, 2022
Paper: Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification

Cross-View Kernel Similarity Metric Learning Using Pairwise Constraints for Person Re-identification T M Feroz Ali, Subhasis Chaudhuri, ICVGIP-20-21

T M Feroz Ali 3 Jun 17, 2022
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Google Cloud Vertex AI Samples Welcome to the Google Cloud Vertex AI sample repository. Overview The repository contains notebooks and community conte

Google Cloud Platform 560 Dec 31, 2022
This is the official repository of XVFI (eXtreme Video Frame Interpolation)

XVFI This is the official repository of XVFI (eXtreme Video Frame Interpolation), https://arxiv.org/abs/2103.16206 Last Update: 20210607 We provide th

Jihyong Oh 195 Dec 29, 2022
TANL: Structured Prediction as Translation between Augmented Natural Languages

TANL: Structured Prediction as Translation between Augmented Natural Languages Code for the paper "Structured Prediction as Translation between Augmen

98 Dec 15, 2022
Weakly Supervised Text-to-SQL Parsing through Question Decomposition

Weakly Supervised Text-to-SQL Parsing through Question Decomposition The official repository for the paper "Weakly Supervised Text-to-SQL Parsing thro

14 Dec 19, 2022
buildseg is a building extraction plugin of QGIS based on PaddlePaddle.

buildseg buildseg is a Building Extraction plugin for QGIS based on PaddlePaddle. How to use Download and install QGIS and clone the repo : git clone

39 Dec 09, 2022
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce

Shen Lab at Texas A&M University 8 Sep 02, 2022
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

DeepKE is a knowledge extraction toolkit supporting low-resource and document-level scenarios for entity, relation and attribute extraction. We provide comprehensive documents, Google Colab tutorials

ZJUNLP 1.6k Jan 05, 2023