Sketch-Based Image Retrieval

34 papers with code • 3 benchmarks • 4 datasets

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Most implemented papers

Deep Shape Matching

janesjanes/sketchy ECCV 2018

We cast shape matching as metric learning with convolutional networks.

Variational Interaction Information Maximization for Cross-domain Disentanglement

gr8joo/IIAE NeurIPS 2020

Grounded in information theory, we cast the simultaneous learning of domain-invariant and domain-specific representations as a joint objective of multiple information constraints, which does not require adversarial training or gradient reversal layers.

Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval

ymcidence/DeepSketchHashing CVPR 2017

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images.

SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks

val-iisc/sketch-parse 5 Sep 2017

We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.

Sketching out the Details: Sketch-based Image Retrieval using Convolutional Neural Networks with Multi-stage Regression

TuBui/SBIR_regression 1 Dec 2017

We propose and evaluate several deep network architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task.

Zero-Shot Sketch-Image Hashing

ymcidence/Zero-Shot-Sketch-Image-Hashing CVPR 2018

As an important part of ZSIH, we formulate a generative hashing scheme in reconstructing semantic knowledge representations for zero-shot retrieval.

A Zero-Shot Framework for Sketch-based Image Retrieval

ShivaKrishnaM/ZS-SBIR 31 Jul 2018

In this paper, we propose a new benchmark for zero-shot SBIR where the model is evaluated in novel classes that are not seen during training.

Universal Perceptual Grouping

KeLi-SketchX/Universal-sketch-perceptual-grouping 7 Aug 2018

In this work we aim to develop a universal sketch grouper.

Generative Domain-Migration Hashing for Sketch-to-Image Retrieval


The generative model learns a mapping that the distributions of sketches can be indistinguishable from the distribution of natural images using an adversarial loss, and simultaneously learns an inverse mapping based on the cycle consistency loss in order to enhance the indistinguishability.

Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax

Ben-Louis/SBIR-DASE-MEMS 11 Dec 2018

This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation.