More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

Overview

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval, CVPR 2021.

Ayan Kumar Bhunia, Pinaki nath Chowdhury, Aneeshan Sain, Yongxin Yang, Tao Xiang, Yi-Zhe Song, “More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval”, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

SketchX_ShoeV2/ChairV2 Dataset: Download

Abstract

A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs. Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced. In this paper, we aim to mitigate such an upper-bound on sketch data, and study whether unlabelled photos alone (of which they are many) can be cultivated for performances gain. In particular, we introduce a novel semi-supervised framework for cross-modal retrieval that can additionally leverage large-scale unlabelled photos to account for data scarcity. At the centre of our semi-supervision design is a sequential photo-to-sketch generation model that aims to generate paired sketches for unlabelled photos. Importantly, we further introduce a discriminator guided mechanism to guide against unfaithful generation, together with a distillation loss based regularizer to provide tolerance against noisy training samples. Last but not least, we treat generation and retrieval as two conjugate problems, where a joint learning procedure is devised for each module to mutually benefit from each other. Extensive experiments show that our semi-supervised model yields significant performance boost over the state-of-the-art supervised alternatives, as well as existing methods that can exploit unlabelled photos for FG-SBIR.

Outline

Outline

Figure: Our proposed method additionally leverages large scale photos without any manually labelled paired sketches to improve FG-SBIR performance. Moreover, we show that the two conjugate process, photo-to-sketch generation and fine-grained SBIR, could improve each other by joint training.

Joint Architecture

Framework Figure: Our framework: a FG-SBIR model leverages large scale unlabelled photos using a sequential photo-to-sketch generation model along with labelled pairs. Discriminator guided instance-wise weighting and distillation loss are used to guard against the noisy generated data. Simultaneously, photo-to-sketch generation model learns by taking reward from FG-SBIR model and Discriminator via policy gradient (over both labelled and unlabelled) together with supervised VAE loss over labelled data. Note rasterization (vector to raster format) is a non-differentiable operation.

Examples

Framework Figure: Qualitative results on our photo-to-sketch generation process, where sketch is shown with attention-map at progressive instances.

Citation

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

@InProceedings{semi-fgsbir,
author = {Ayan Kumar Bhunia and Pinaki Nath Chowdhury and Aneeshan Sain and Yongxin Yang and Tao Xiang and Yi-Zhe Song},
title = {More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}

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
mlpack: a scalable C++ machine learning library --

a fast, flexible machine learning library Home | Documentation | Doxygen | Community | Help | IRC Chat Download: current stable version (3.4.2) mlpack

mlpack 4.2k Jan 09, 2023
RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020)

RCDNet: A Model-driven Deep Neural Network for Single Image Rain Removal (CVPR2020) Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng [PDF] [Supplementary M

Hong Wang 6 Sep 27, 2022
GNN-based Recommendation Benchma

GRecX A Fair Benchmark for GNN-based Recommendation Preliminary Comparison DiffNet-Yelp dataset (featureless) Algo 73 Oct 17, 2022

MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 01, 2022
Optimizers-visualized - Visualization of different optimizers on local minimas and saddle points.

Optimizers Visualized Visualization of how different optimizers handle mathematical functions for optimization. Contents Installation Usage Functions

Gautam J 1 Jan 01, 2022
Implementation of algorithms for continuous control (DDPG and NAF).

DEPRECATION This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac.

Ilya Kostrikov 288 Dec 31, 2022
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

TorchRL Disclaimer This library is not officially released yet and is subject to change. The features are available before an official release so that

Meta Research 860 Jan 07, 2023
2D Time independent Schrodinger equation solver for arbitrary shape of well

Schrodinger Well Python Python solver for timeless Schrodinger equation for well with arbitrary shape https://imgur.com/a/jlhK7OZ Pictures of circular

WeightAn 24 Nov 18, 2022
I will implement Fastai in each projects present in this repository.

DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea

Thinam Tamang 43 Dec 20, 2022
PyTorch implementation of TSception V2 using DEAP dataset

TSception This is the PyTorch implementation of TSception V2 using DEAP dataset in our paper: Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai

Yi Ding 27 Dec 15, 2022
The official implementation of Variable-Length Piano Infilling (VLI).

Variable-Length-Piano-Infilling The official implementation of Variable-Length Piano Infilling (VLI). (paper: Variable-Length Music Score Infilling vi

29 Sep 01, 2022
Implementation of the state of the art beat-detection, downbeat-detection and tempo-estimation model

The ISMIR 2020 Beat Detection, Downbeat Detection and Tempo Estimation Model Implementation. This is an implementation in TensorFlow to implement the

Koen van den Brink 1 Nov 12, 2021
Official implementation for CVPR 2021 paper: Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detection This repo is the official implementation for CVPR 2021 paper: Adaptive Class Suppressio

CASIA-IVA-Lab 67 Dec 04, 2022
Accelerated Multi-Modal MR Imaging with Transformers

Accelerated Multi-Modal MR Imaging with Transformers Dependencies numpy==1.18.5 scikit_image==0.16.2 torchvision==0.8.1 torch==1.7.0 runstats==1.8.0 p

54 Dec 16, 2022
A small tool to joint picture including gif

README 做设计的时候遇到拼接长图的情况,但是发现没有什么好用的能拼接gif的工具。 于是自己写了个gif拼接小工具。 可以自动拼接gif、png和jpg等常见格式。 效果 从上至下 从下至上 从左至右 从右至左 使用 克隆仓库 git clone https://github.com/Dels

3 Dec 15, 2021
This is the implementation of the paper LiST: Lite Self-training Makes Efficient Few-shot Learners.

LiST (Lite Self-Training) This is the implementation of the paper LiST: Lite Self-training Makes Efficient Few-shot Learners. LiST is short for Lite S

Microsoft 28 Dec 07, 2022
Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

FAC-Net Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization Linjiang Huang (CUHK), Liang Wang (CASIA), Hongsheng

21 Nov 22, 2022
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping

LVI-SAM This repository contains code for a lidar-visual-inertial odometry and mapping system, which combines the advantages of LIO-SAM and Vins-Mono

Tixiao Shan 1.1k Dec 27, 2022