Sketch-Based Image Retrieval
36 papers with code • 3 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Sketch-Based Image Retrieval
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Chairs
Quick, Draw! Dataset
ShoeV2
QuickDraw-Extended
Most implemented papers
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks.
Variational Interaction Information Maximization for Cross-domain Disentanglement
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
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
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
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
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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
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
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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
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.
