提出无序细胞表示，提升表格多任务识别一致性与速度。
Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
- 用非因果注意力构建全局上下文感知的细胞特征
- 在两个数据集上实现定位与端到端识别性能提升，推理提速三倍
- 适合需要高效高精度表格理解的工业场景
多任务表格识别在统一框架下同时处理表格结构预测、单元格定位和内容识别。现有方法依赖自回归解码器生成结构并复用隐藏状态进行定位与识别，导致单元格表示受生成顺序影响，降低全局一致性。本文提出结构优化模块，通过非因果注意力生成与顺序无关的单元格特征，支持并行内容推断，同时使每个单元格依赖于重构特征中的全局上下文。在两个大规模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均取得一致提升，整体推理时间减少约三倍。
原文摘要 · Abstract (English)
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.
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