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Found 14 papers, 6 papers with code
Wired Perspectives: Multi-View Wire Art Embraces Generative AI
Creating multi-view wire art (MVWA), a static 3D sculpture with diverse interpretations from different viewpoints, is a complex task even for skilled artists.
SketchXAI: A First Look at Explainability for Human Sketches
Following this, we design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes: shape, location, and order.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Photo Pre-Training, but for Sketch
This lack of sketch data has imposed on the community a few "peculiar" design choices -- the most representative of them all is perhaps the coerced utilisation of photo-based pre-training (i. e., no sketch), for many core tasks that otherwise dictates specific sketch understanding.
Finding Badly Drawn Bunnies
Our key discovery lies in exploiting the magnitude (L2 norm) of a sketch feature as a quantitative quality metric.
SketchAA: Abstract Representation for Abstract Sketches
The superiority of explicitly abstracting sketch representation is empirically validated on a number of sketch analysis tasks, including sketch recognition, fine-grained sketch-based image retrieval, and generative sketch healing.
Your "Flamingo" is My "Bird": Fine-Grained, or Not
For that, we re-envisage the traditional setting of FGVC, from single-label classification, to that of top-down traversal of a pre-defined coarse-to-fine label hierarchy -- so that our answer becomes "bird"-->"Phoenicopteriformes"-->"Phoenicopteridae"-->"flamingo".
Ranked #16 on
Fine-Grained Image Classification
on FGVC Aircraft
Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval
ImageNet pre-training has long been considered crucial by the fine-grained sketch-based image retrieval (FG-SBIR) community due to the lack of large sketch-photo paired datasets for FG-SBIR training.
Generalising Fine-Grained Sketch-Based Image Retrieval
This manifold can then be used to paramaterise the learning of a sketch/photo representation.
Deep Factorised Inverse-Sketching
Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included.
Learning to Sketch with Shortcut Cycle Consistency
In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process.
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.
