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
Latest papers with no code
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%.
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not
At the very core of our solution is a prompt learning setup.
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches
We further introduce specific designs to tackle the abstract nature of human sketches, including a fine-grained discriminative loss on the back of a trained sketch-photo retrieval model, and a partial-aware sketch augmentation strategy.
Ontology-aware Network for Zero-shot Sketch-based Image Retrieval
Zero-Shot Sketch-Based Image Retrieval (ZSSBIR) is an emerging task.
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task.
Democratising 2D Sketch to 3D Shape Retrieval Through Pivoting
We perform pivoting on two existing datasets, each from a distant research domain to the other: 2D sketch and photo pairs from the sketch-based image retrieval field (SBIR), and 3D shapes from ShapeNet.
Transformers and CNNs both Beat Humans on SBIR
Sketch-based image retrieval (SBIR) is the task of retrieving natural images (photos) that match the semantics and the spatial configuration of hand-drawn sketch queries.
Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval
To narrow the domain differences between sketches and images, we extract edge maps for natural images and treat them as a bridge between images and sketches, which have similar content to images and similar style to sketches.
Partially Does It: Towards Scene-Level FG-SBIR with Partial Input
We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial".
Sketch3T: Test-Time Training for Zero-Shot SBIR
In this paper, we question to argue that this setup by definition is not compatible with the inherent abstract and subjective nature of sketches, i. e., the model might transfer well to new categories, but will not understand sketches existing in different test-time distribution as a result.
