Sketch classification and classification-driven analysis using Fisher vectors

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Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015, methodology): “Schneider and Tuytelaars [30] cleaned up the dataset by removing instances and categories that humans find hard to recognize.”
Sketch-a-Net: A Deep Neural Network that Beats Humans (2016, methodology): “The following observations can be made: (i) Sketch-a-Net significantly outperforms all existing methods purposefully designed for sketch (Eitz et al, 2012; Li et al, 2013; Schneider and Tuytelaars, 2014), as well as the state-of-the-art photo-oriented CNN model (Krizhevsky et al, 2012) re-purposed f”
Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval (2017, background): “With the proliferation of touch-screen devices, a number of sketch-based computer vision problems have attracted increasing attention, including sketch recognition [47, 36, 3, 32], sketch-based image retrieval [46, 24, 10], sketch-based 3D model retrieval [39], and forensic sketch analysis [14, 28].”
Sketchformer: Transformer-Based Representation for Sketched Structure (2020, unknown): “bag of words) [17, 18, 19].”