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Found 4 papers, 1 papers with code
Progressive Evolution from Single-Point to Polygon for Scene Text
We demonstrate the accuracy of the generated polygons through extensive experiments: 1) By creating polygons from ground truth points, we achieved an accuracy of 82. 0% on ICDAR 2015; 2) In training detectors with polygons generated by our method, we attained 86% of the accuracy relative to training with ground truth (GT); 3) Additionally, the proposed Point2Polygon can be seamlessly integrated to empower single-point spotters to generate polygons.
Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis
To our knowledge, ours is the first scheme for this challenging task, including model, training, and evaluation.
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition
Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-query cross-attention mechanism.
Person Re-Identification by Unsupervised Video Matching
Crucially, this model does not require pairwise labelled training data (i. e. unsupervised) therefore readily scalable to large scale camera networks of arbitrary camera pairs without the need for exhaustive data annotation for every camera pair.
