DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
Document Layout Analysis;Document Pretraining;Document Understanding
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- The small initial dataset is come from $M^6Doc$ test, why not choose $M^6doc$ training part as initial dataset then perform test on $M^6Doc$ test?
- What role does stratified sampling play in selecting elements from the pool?
-The paper introduces a new synthesized dataset, DocSynTH-300K, based on M6Doc, offering an approach for generating diverse layouts.
-Experiments conducted on in-house data demonstrate the effectiveness of the proposed method.
-The figures and charts are visually appealing.
The paper introduces the Mesh-candidate BestFit algorithm to generate the large-scale DocSynth-300K dataset, which improves fine-tuning results across diverse document types. It also proposes a Global-to-Local Controllable Receptive Module to effectively handle multi-scale variations in document elements. Experimental results show that DocLayout-YOLO delivers good performance in both speed and accuracy on  $D^4LA$, DocLayNet and in-house datasets DocStructBench.
- The authors state in line 153 on page 3 that "unimodal pre-training datasets are characterized by significant homogeneity, primarily comprising academic papers." This statement is inaccurate. In fact, there are already datasets from diverse domains, such as M6Doc, which is a large-scale, multi-domain dataset. Moreover, the authors’ own DocSynth-300k dataset is constructed based on M6Doc, further illustrating the availability of varied data sources.
- The description of layout generation is not very clear. How does the candidate sampling and meshgrid construction process ensure that elements are placed effectively on a page layout without overlapping? 
- The experiments conducted on in-house data are not very convincing. It would be better to include a different backbone model pre-trained on DocSynth-300k and test it on public datasets.
- Lack of comparison with SOTA: "M2Doc: A Multi-Modal Fusion Approach for Document Layout Analysis,AAAI 2024"
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See the weaknesses.
This paper makes two main contributions. First, it introduces the Mesh-candidate BestFit algorithm for robust document pre-training, generating the large-scale DocSynth-300K dataset which improves fine-tuning performance across various document types. Second, it proposes DocLayout-YOLO with a Global-to-Local Controllable Receptive Module for model optimization, enabling better handling of multi-scale variations in document elements and achieving excellent performance in both speed and accuracy.
The paper presents two contributions. On one hand, it devises the Mesh-candidate BestFit algorithm to create the DocSynth-300K dataset for enhanced document pre-training. On the other hand, it proposes a Global-to-Local Controllable Receptive Module for model optimization.
The author's contributions in terms of both data and model design seem acceptable. As I am not in this field, I still need to refer to the opinions of other reviewers and the chair. I have some concerns as follows.

1. When the author introduces the two types of methods in the introduction, references should be added. Furthermore, you can introduce how some classic methods are done.

2. The author is extremely reluctant to add references throughout the paper, which is very unfriendly to researchers who are not in this field. In addition, references should also be added in the tables. Otherwise, it is not clear whether these are the author's own reproductions or results from other papers.

3. The performance comparisons in Table 2, Table 3, and Table 4 are unfair as the pre-training data of each method is different.

4. In various other fields, the feature fusion of global-local seems to be a common practice and there is nothing new about it.

5. The author seems not to have mentioned Figure 4 in the manuscript.
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There are no ethical concerns related to this paper.
No ethics review needed.
1. Motivation for creating a large-scale structured document pretraining dataset?
2. Why the recently proposed vision-based unimodal works has been ignored?
3. How does the model tackle overlap and unstructured documents?
4. Effectiveness of GL-CRM during scaling and with larger models
This paper has the following two contributions:
1. A large-scale diverse document pertaining dataset with Mesh-candidate best-fit algorithm.
2. Model optimization with Global-to-local Controllable Receptive Module.
This work DocLayout-YOLO proposes a new technique of document layout analysis. First, they have created a large-scale dataset DocSynth-300K for robust document-specific pre-training where they synthesize documents as a 2D bin packing problem. They solve it with the Mesh-candidate BestFit algorithm where they treat available grids built by the input layout as bins of different sizes and iteratively perform the best matching to generate more diverse and reasonable document layouts. Also, they improve the existing YOLO-v10 through a global-to-local controllable receptive module for better optimization.
1. The main concern is the motivation of the paper. In document layout analysis the problem isn't getting better performance for the well-defined class with structured layout. In contrast, the goal is to improve the performance of small-scale diverse datasets like the following:
PRIMA: https://www.primaresearch.org/datasets/Layout_Analysis
Historical Japanese dataset: https://dell-research-harvard.github.io/HJDataset/
There are no particular contributions regarding the performance improvement on these small-scale diverse datasets.

2. In the related works, Some Unimodal works have not been cited, which are pretty effective and faster compared to the proposed one.
SwinDocSegmenter: https://arxiv.org/abs/2305.04609
SemiDocSeg: https://link.springer.com/article/10.1007/s10032-024-00473-y
GraphKD: https://arxiv.org/abs/2402.11401

3. The main drawback of Meshgrid construction is the layout will be always structured (i.e. it ensures all the document elements must have a non-overlapping rectangular region). How they tackle complex layout which has overlapping region and unstructured documents like magazines and comics.

4. How does the GL-CRM impact computational efficiency, especially for larger models or when scaling? As it is just a combination of GeLU activation over batch-normalization. Why do other activation fails? Does LayerNorm can also affect performance?
Yes
No ethics review needed.
Why doesn't the author synthesize more pre-training data to demonstrate effectiveness?
1, The proposed GLOBAL-TO-LOCAL method can improve the accuracy of Yolo in layout analysis tasks.
2, The proposed methodology termed Mesh-candidate BestFit can automatically synthesize diverse documents.
This paper proposes DocLayout-YOLO for diverse layout analysis tasks. In addition, this paper also proposes Mesh condition BestFit for synthesizing layout analysis datasets, and proposes GL-CRM to further improve the accuracy of the model.
But I think this paper has significant flaws:
1. The author's experimental results are not convincing. The author claims that their results achieved SOTA, but did not compare with better models in the DocLayNet dataset, such as VGT (Da, Cheng, et al. "Vision grid transformer for document layout analysis." ICCV 2023.), GLAM  (Wang, Jilin, et al. "A graphical approach to document layout analysis." ICDAR 2023.), etc. These models have achieved better accuracy on DocLayNet.
2. In addition, the author did not compare the results with other experiments on larger benchmarks (DocBank, PublayNet). These datasets are authoritative benchmarks, similar to COCO in general object detection. However, the author's results on the in-house dataset DocStructBench lack authority.
3. The dataset DocSynth-300K synthesized by the author is not large enough, and some publicly available datasets such as DocBank possesses 500K data. Since the author has proposed a method for synthesizing datasets, the amount of data should not be less than some open-source datasets, and may even be more than 3 times larger than the largest dataset. Because the pre-trained dataset needs to be much larger than the fine-tuned dataset to demonstrate the effectiveness.
4. The innovation of this paper is limited, and the proposed model is based on Yolo without significant structural innovation.
@misc{
zhao2024doclayoutyolo,
title={DocLayout-{YOLO}: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception},
author={Zhiyuan Zhao and Hengrui Kang and Bin Wang and Conghui He},
year={2024},
url={https://openreview.net/forum?id=k0X4m9GAQV}
}
Document Layout Analysis is crucial for real-world document understanding systems, but it encounters a challenging trade-off between speed and accuracy: multimodal methods leveraging both text and visual features achieve higher accuracy but suffer from significant latency, whereas unimodal methods relying solely on visual features offer faster processing speeds at the expense of accuracy. To address this dilemma, we introduce DocLayout-YOLO, a novel approach that enhances accuracy while maintaining speed advantages through document-specific optimizations in both pre-training and model design. For robust document pre-training, we introduce the Mesh-candidate BestFit algorithm, which frames document synthesis as a two-dimensional bin packing problem, generating the large-scale, diverse DocSynth-300K dataset. Pre-training on the resulting DocSynth-300K dataset significantly improves fine-tuning performance across various document types. In terms of model optimization, we propose a Global-to-Local Controllable Receptive Module that is capable of better handling multi-scale variations of document elements. Furthermore, to validate performance across different document types, we introduce a complex and challenging benchmark named DocStructBench. Extensive experiments on downstream datasets demonstrate that DocLayout-YOLO excels in both speed and accuracy. Code, data, and models will be made publicly available.
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Document Layout Analysis Document Pretraining Document Understanding
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zhao|doclayoutyolo_enhancing_document_layout_analysis_through_diverse_synthetic_data_and_globaltolocal_adaptive_perception
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applications to computer vision, audio, language, and other modalities
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DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
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