Table detection within document images is a crucial task in document
processing, involving the identification and localization of tables. Recent
strides in deep learning have substantially improved the accuracy of this task,
but it still heavily relies on large labeled datasets for effective training.
Several semi-supervised approaches have emerged to overcome this challenge,
often employing CNN-based detectors with anchor proposals and post-processing
techniques like non-maximal suppression (NMS). However, recent advancements in
the field have shifted the focus towards transformer-based techniques,
eliminating the need for NMS and emphasizing object queries and attention
mechanisms. Previous research has focused on two key areas to improve
transformer-based detectors: refining the quality of object queries and
optimizing attention mechanisms. However, increasing object queries can
introduce redundancy, while adjustments to the attention mechanism can increase
complexity. To address these challenges, we introduce a semi-supervised
approach employing SAM-DETR, a novel approach for precise alignment between
object queries and target features. Our approach demonstrates remarkable
reductions in false positives and substantial enhancements in table detection
performance, particularly in complex documents characterized by diverse table
structures. This work provides more efficient and accurate table detection in
semi-supervised settings.

通过 SAM-DETR 引入半监督方法，实现了目标查询和目标特征之间的精确对齐，从而在表的识别方面显著降低了误报率，并在复杂文档中表现出较高的性能，提供了更加高效准确的表格检测。

朝着端到端的半监督表格检测与语义对齐匹配转换器

Towards End-to-End Semi-Supervised Table Detection with Semantic Aligned  Matching Transformer

The recently developed DEtection TRansformer (DETR) establishes a new object
detection paradigm by eliminating a series of hand-crafted components. However,
DETR suffers from extremely slow convergence, which increases the training cost
significantly. We observe that the slow convergence is largely attributed to
the complication in matching object queries with target features in different
feature embedding spaces. This paper presents SAM-DETR, a
Semantic-Aligned-Matching DETR that greatly accelerates DETR's convergence
without sacrificing its accuracy. SAM-DETR addresses the convergence issue from
two perspectives. First, it projects object queries into the same embedding
space as encoded image features, where the matching can be accomplished
efficiently with aligned semantics. Second, it explicitly searches salient
points with the most discriminative features for semantic-aligned matching,
which further speeds up the convergence and boosts detection accuracy as well.
Being like a plug and play, SAM-DETR complements existing convergence solutions
well yet only introduces slight computational overhead. Extensive experiments
show that the proposed SAM-DETR achieves superior convergence as well as
competitive detection accuracy. The implementation codes are available at
this https URL

SAM-DETR 通过解决不同特征嵌入空间中对象查询与目标特征匹配的复杂性问题，实现了语义对齐匹配，加速了收敛速度并提高了检测精度。