Sketchformer: Transformer-based Representation for Sketched Structure

Autor:	Ribeiro, Leo Sampaio Ferraz, Bui, Tu, Collomosse, John, Ponti, Moacir
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report several variants exploring continuous and tokenized input representations, and contrast their performance. Our learned embedding, driven by a dictionary learning tokenization scheme, yields state of the art performance in classification and image retrieval tasks, when compared against baseline representations driven by LSTM sequence to sequence architectures: SketchRNN and derivatives. We show that sketch reconstruction and interpolation are improved significantly by the Sketchformer embedding for complex sketches with longer stroke sequences. Comment: Accepted for publication at CVPR 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.10381 Zobrazit plný text záznamu View this record from Arxiv