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Found 3 papers, 1 papers with code
ChartReformer: Natural Language-Driven Chart Image Editing
Chart visualizations are essential for data interpretation and communication; however, most charts are only accessible in image format and lack the corresponding data tables and supplementary information, making it difficult to alter their appearance for different application scenarios.
Player Re-Identification Using Body Part Appearences
Our model consists of a two-stream network (one stream for appearance map extraction and the other for body part map extraction) and a bilinear-pooling layer that generates and spatially pools the body part map.
LineFormer: Rethinking Line Chart Data Extraction as Instance Segmentation
Existing works, however, are not robust to all these variations, either taking an all-chart unified approach or relying on auxiliary information such as legends for line data extraction.
