First adaptation of Uncertainty Herding to cascaded TD-TSR pipelines via RankFusion and CAPA extensions yields consistent gains over baselines on four table extraction datasets under annotation budgets of 71-500 documents.
ACM Computing Surveys56, 1 – 41 (2022), https://api.semanticscholar.org/CorpusID:253553399
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Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
First adaptation of Uncertainty Herding to cascaded TD-TSR pipelines via RankFusion and CAPA extensions yields consistent gains over baselines on four table extraction datasets under annotation budgets of 71-500 documents.