重新审视自回归多任务表格识别中的结构依赖性：基于顺序无关的单元格级表示
Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
AI总结：
针对自回归多任务表格识别中单元格表示顺序依赖导致全局一致性下降的问题，提出通过非因果注意力生成顺序无关的单元格特征，实现并行推理，在两大数据集上提升定位与识别性能，推理时间减少约3倍。
摘要：
多任务表格识别在统一框架中联合处理表格结构预测、单元格定位和单元格内容识别。现有方法通常依赖自回归解码器生成表格结构，并重用其隐藏状态进行单元格定位和内容识别。这种自回归生成过程可能使单元格表示产生顺序依赖，降低跨单元格的全局一致性。本文提出一个结构细化模块，通过非因果注意力产生顺序无关的单元格特征。该设计使得单元格内容能够并行推理，同时每个单元格以细化特征中编码的全局上下文为条件。在两个大型数据集上的实验表明，该方法在单元格定位和端到端识别上持续提升，同时将整体推理时间减少约三倍。
英文摘要：
Multi-task table recognition jointly addresses table structure prediction, cell localization, and cell content recognition within a unified framework. Existing approaches often rely on autoregressive decoders to generate table structures and reuse their hidden states for cell localization and content recognition. This autoregressive generation process can make cell representations order-dependent, degrading global consistency across cells. This paper proposes a structural refinement module that produces order-independent cell features through non-causal attention. This design enables parallel inference of cell contents while conditioning each cell on global context encoded in the refined features. Experiments on two large datasets demonstrate consistent gains in cell localization and end-to-end recognition, while reducing overall inference time by around threefold.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
