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Found 6 papers, 1 papers with code
Towards End-to-End Semi-Supervised Table Detection with Semantic Aligned Matching Transformer
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.
A Hybrid Approach for Document Layout Analysis in Document images
This paper navigates the complexities of understanding various elements within document images, such as text, images, tables, and headings.
Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries.
Ranked #1 on
Semi-Supervised Object Detection
on COCO
Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
Upon integrating query modifications in the DETR, we outperform prior works and achieve new state-of-the-art results with the mAP of 96. 9\%, 95. 7\% and 99. 3\% on TableBank, PubLaynet, PubTables, respectively.
Ranked #3 on
Document Layout Analysis
on PubLayNet val
Object Detection with Transformers: A Review
The astounding performance of transformers in natural language processing (NLP) has motivated researchers to explore their applications in computer vision tasks.
Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer
Table detection is the task of classifying and localizing table objects within document images.
