Researchers have adapted an active learning strategy called Uncertainty Herding (UHerding) for cascaded object detection pipelines used in table extraction. This adaptation aims to reduce the costly annotation burden, particularly for Table Structure Recognition (TSR). The proposed extensions, RankFusion and CAPA, leverage the dependency between Table Detection (TD) and TSR stages by incorporating dual-manifold coverage and stage-dependent gating with uncertainty calibration. Experiments on multiple datasets demonstrate that UHerding outperforms baseline methods, with CAPA emerging as a consistent and effective strategy. AI
IMPACT This research could lead to more efficient and cost-effective training of AI models for document analysis and data extraction.
RANK_REASON The cluster contains an academic paper detailing a new method for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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