{"entry_id": "f60f9255-cfb4-41ed-a70c-dc9e31ed3bed", "infos": [{"txt": "Hierarchical Document Parsing via Large Margin Feature Matching and", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 0, "bbox": [[0.135, 0.124, 0.862, 0.138]]}]}, "tags": ["title"], "label": "article_title", "web_segment_id": 1, "global_sentence_id": 0, "edu_l1_label": "BOT"}, {"txt": "Heuristics", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 0, "bbox": [[0.449, 0.145, 0.55, 0.158]]}]}, "tags": ["title"], "label": "article_title", "web_segment_id": 2, "global_sentence_id": 1, "edu_l1_label": "BOT"}, {"txt": "Duong Anh Kiet", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 100000, "bbox": [[0.433, 0.18, 0.57, 0.19]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 3, "global_sentence_id": 2, "edu_l1_label": "EDU_O"}, {"txt": "L3i Laboratory, La Rochelle University", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 200000, "bbox": [[0.37, 0.201, 0.627, 0.213]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 4, "global_sentence_id": 3, "edu_l1_label": "EDU_O"}, {"txt": "17042 La Rochelle Cedex 1- France", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 300000, "bbox": [[0.38, 0.215, 0.618, 0.224]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 5, "global_sentence_id": 4, "edu_l1_label": "EDU_O"}, {"txt": "anh.duong@univ-lr.fr", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 400000, "bbox": [[0.428, 0.232, 0.574, 0.233]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 6, "global_sentence_id": 5, "edu_l1_label": "EDU_O"}, {"txt": "SZ07TIN8[TO.so] CAZttL0.70sC:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.029, 0.248, 0.052, 0.629]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 7, "global_sentence_id": 6, "edu_l1_label": "EDU_O"}, {"txt": "AIXTe", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 500000, "bbox": [[0.052, 0.248, 0.052, 0.677]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 7, "global_sentence_id": 7, "edu_l1_label": "EDU_O"}, {"txt": "Abstract", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 600000, "bbox": [[0.252, 0.277, 0.313, 0.285]]}]}, "tags": ["title"], "label": "abstract", "web_segment_id": 8, "global_sentence_id": 8, "edu_l1_label": "BOS"}, {"txt": "We present our solution to the AAAI-25 VRD-IU challenge,achieving first place in the competition.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.104, 0.296, 0.463, 0.306], [0.105, 0.311, 0.345, 0.317]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 9, "global_sentence_id": 9, "edu_l1_label": "IOS"}, {"txt": "Our approach inte-grates large margin loss for improved feature discrimination and employs heuristic rules to refine hierarchical relation-ships.BBy combining a deep learning-based matching strat-egy with greedy algorithms, we achieve a significant boost in accuracy while maintaining computational efficiency.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.345, 0.311, 0.46, 0.316], [0.104, 0.324, 0.46, 0.33], [0.11, 0.336, 0.461, 0.342], [0.104, 0.348, 0.462, 0.352], [0.104, 0.362, 0.462, 0.367], [0.105, 0.373, 0.435, 0.379]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 9, "global_sentence_id": 10, "edu_l1_label": "IOS"}, {"txt": "Our method attains an accuracy of 0.98904 on the private leader-board, demonstrating its effectiveness in document structure parsing.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 700000, "bbox": [[0.435, 0.373, 0.461, 0.379], [0.104, 0.386, 0.462, 0.39], [0.105, 0.397, 0.46, 0.405], [0.104, 0.412, 0.153, 0.417]]}]}, "tags": ["text"], "label": "abstract", "web_segment_id": 9, "global_sentence_id": 11, "edu_l1_label": "IOS"}, {"txt": "Code-https://github.com/ffyyytt/VRUID-AAAI-DAKiet Models-", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.087, 0.431, 0.478, 0.441], [0.087, 0.448, 0.159, 0.454]]}]}, "tags": ["text"], "label": "author", "web_segment_id": 10, "global_sentence_id": 12, "edu_l1_label": "EDU_O"}, {"txt": "https://www.kaggle.com/models/fdfyaytkt/vruid-aaai-dakiet/PyTorch/figure", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 800000, "bbox": [[0.104, 0.462, 0.458, 0.468], [0.104, 0.476, 0.246, 0.485]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 11, "global_sentence_id": 13, "edu_l1_label": "EDU_O"}, {"txt": "Introduction", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 900000, "bbox": [[0.229, 0.505, 0.335, 0.516]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 12, "global_sentence_id": 14, "edu_l1_label": "BOS"}, {"txt": "Document structure parsing is essential for visually rich document understanding, enabling applications such as in-formation retrieval and document summarization (Xu et al.2020).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.087, 0.523, 0.476, 0.533], [0.087, 0.538, 0.478, 0.547], [0.088, 0.553, 0.478, 0.561], [0.087, 0.565, 0.132, 0.574]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 13, "global_sentence_id": 15, "edu_l1_label": "IOS"}, {"txt": "Unlike traditional text-based document processing,mining reports and other complex documents contain het-erogeneous semantic entities,requiring hierarchical parsing to infer parent-child relationships (Li et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.132, 0.565, 0.48, 0.575], [0.087, 0.582, 0.478, 0.585], [0.087, 0.596, 0.476, 0.604], [0.087, 0.609, 0.368, 0.616]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 13, "global_sentence_id": 16, "edu_l1_label": "IOS"}, {"txt": "2020).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.368, 0.609, 0.413, 0.616]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 13, "global_sentence_id": 17, "edu_l1_label": "IOS"}, {"txt": "The AAAI-25 Visually-Rich Document (VRD-IU) Leader-board task involves predicting parent-child relationships among detected entities in mining reports.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.105, 0.621, 0.48, 0.628], [0.087, 0.635, 0.476, 0.644], [0.087, 0.652, 0.382, 0.658]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 14, "global_sentence_id": 18, "edu_l1_label": "IOS"}, {"txt": "This problem is challenging due to scanned and inconsistently structured documents, necessitating inference from spatial and contex-tual cues.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.382, 0.652, 0.47, 0.657], [0.09, 0.664, 0.478, 0.671], [0.087, 0.677, 0.479, 0.684], [0.087, 0.692, 0.151, 0.698]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 14, "global_sentence_id": 19, "edu_l1_label": "IOS"}, {"txt": "Performance is evaluated using accuracy.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.151, 0.692, 0.423, 0.698]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 14, "global_sentence_id": 20, "edu_l1_label": "IOS"}, {"txt": "In this paper, we present our solution, which ranked first in the competition.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.104, 0.704, 0.48, 0.713], [0.087, 0.718, 0.215, 0.727]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 15, "global_sentence_id": 21, "edu_l1_label": "IOS"}, {"txt": "We combines large margin loss for im-proved feature discrimination with heuristic greedy algo-rithms for efficient hierarchical assignment.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.215, 0.718, 0.475, 0.726], [0.087, 0.734, 0.478, 0.739], [0.088, 0.747, 0.375, 0.754]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 15, "global_sentence_id": 22, "edu_l1_label": "IOS"}, {"txt": "Method details our approach, Experiments presents experimental results,and Conclusion section concludes with insights and future directions.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1000000, "bbox": [[0.375, 0.747, 0.478, 0.754], [0.087, 0.762, 0.48, 0.769], [0.087, 0.776, 0.476, 0.782], [0.087, 0.787, 0.158, 0.796]]}]}, "tags": ["text"], "label": "introduction", "web_segment_id": 15, "global_sentence_id": 23, "edu_l1_label": "IOS"}, {"txt": "Related Work", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1100000, "bbox": [[0.224, 0.813, 0.341, 0.824]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 16, "global_sentence_id": 24, "edu_l1_label": "BOS"}, {"txt": "Documents inherently exhibit a hierarchical structure, where parent-child relationships are fundamental across various", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1200000, "bbox": [[0.087, 0.831, 0.477, 0.84], [0.087, 0.848, 0.476, 0.854]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 17, "global_sentence_id": 25, "edu_l1_label": "IOS"}, {"txt": "Copyright $C$ 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.086, 0.864, 0.48, 0.889]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 18, "global_sentence_id": 26, "edu_l1_label": "EDU_O"}, {"txt": "All rights reserved.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.086, 0.864, 0.48, 0.889]]}]}, "tags": ["text"], "label": "O", "web_segment_id": 18, "global_sentence_id": 27, "edu_l1_label": "EDU_O"}, {"txt": "formats, such as figure-caption and section-paragraph asso-ciations in reports (Ding, Lee, and Han 2024).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.521, 0.277, 0.911, 0.284], [0.521, 0.292, 0.837, 0.299]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 19, "global_sentence_id": 28, "edu_l1_label": "IOS"}, {"txt": "Accurately modeling these relationships is crucial for document struc-ture parsing.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.837, 0.292, 0.91, 0.301], [0.522, 0.307, 0.913, 0.31], [0.521, 0.32, 0.609, 0.327]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 19, "global_sentence_id": 29, "edu_l1_label": "IOS"}, {"txt": "Prior works such as DocStruct (Wang et al.2020),SERA (Zhang et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.609, 0.32, 0.912, 0.327], [0.521, 0.331, 0.694, 0.341]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 19, "global_sentence_id": 30, "edu_l1_label": "IOS"}, {"txt": "2021), and KVPFormer (Hu et al.2023) have significantly contributed to this task by leverag-ing deep learning models for entity linking.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.694, 0.331, 0.913, 0.341], [0.521, 0.345, 0.911, 0.355], [0.522, 0.36, 0.81, 0.368]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 19, "global_sentence_id": 31, "edu_l1_label": "IOS"}, {"txt": "However, these methods rely on conventional distance metrics,which may struggle to differentiate entities with highly similar feature representations (Duong and Gomez-Krämer 2025b).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1300000, "bbox": [[0.81, 0.36, 0.91, 0.367], [0.522, 0.375, 0.91, 0.383], [0.522, 0.389, 0.911, 0.395], [0.521, 0.403, 0.863, 0.409]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 19, "global_sentence_id": 32, "edu_l1_label": "IOS"}, {"txt": "Recent advancements in matching take inspiration from CLIP (Radford et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.52, 0.415, 0.913, 0.553]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 33, "edu_l1_label": "IOS"}, {"txt": "2021), instead of cross-modal learn-ing between text and images, document structure parsing deals with entities of the same modality.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.52, 0.415, 0.913, 0.553]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 34, "edu_l1_label": "IOS"}, {"txt": "However, we also need feature representations for child $fc$ and for parent $fp$.In terms of feature separation, large margin loss functions such as LMCot (Duong, Nguyen, and Truong 2022), Arc-Face (Deng et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.52, 0.415, 0.913, 0.553]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 35, "edu_l1_label": "IOS"}, {"txt": "2019), and NormFace (Wang et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.52, 0.415, 0.913, 0.553]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 36, "edu_l1_label": "IOS"}, {"txt": "2017)improve feature extraction by enforcing larger inter-class separability while maintaining intra-class compactness.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1400000, "bbox": [[0.52, 0.415, 0.913, 0.553]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 37, "edu_l1_label": "IOS"}, {"txt": "Beyond deep learning-based approaches, heuristic rules and greedy algorithms have proven effective in competitive settings(Duong and Gomez-Krämer 2025a).", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.538, 0.553, 0.91, 0.562], [0.521, 0.569, 0.911, 0.576], [0.522, 0.583, 0.81, 0.589]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 21, "global_sentence_id": 38, "edu_l1_label": "IOS"}, {"txt": "In the next sec-tion, we detail our integration of large margin loss for match-ing and the heuristic rules applied in the competition.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1500000, "bbox": [[0.81, 0.583, 0.911, 0.587], [0.522, 0.597, 0.911, 0.601], [0.524, 0.61, 0.87, 0.617]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 21, "global_sentence_id": 39, "edu_l1_label": "IOS"}, {"txt": "Method", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1600000, "bbox": [[0.684, 0.636, 0.748, 0.646]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 22, "global_sentence_id": 40, "edu_l1_label": "BOS"}, {"txt": "In this section we introduce our loss function with improve-ments from large margin loss.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1700000, "bbox": [[0.521, 0.655, 0.913, 0.664], [0.522, 0.672, 0.722, 0.677]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 23, "global_sentence_id": 41, "edu_l1_label": "IOS"}, {"txt": "And then the heuristic rules we observed to increase the performance of the model.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1700000, "bbox": [[0.722, 0.672, 0.911, 0.678], [0.522, 0.684, 0.879, 0.691]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 23, "global_sentence_id": 42, "edu_l1_label": "IOS"}, {"txt": "Margin loss for matching", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1800000, "bbox": [[0.521, 0.708, 0.713, 0.719]]}]}, "tags": ["text"], "label": "title1", "web_segment_id": 24, "global_sentence_id": 43, "edu_l1_label": "IOS"}, {"txt": "Starting with the loss function in CLIP (Radford et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.521, 0.727, 0.868, 0.735]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 25, "global_sentence_id": 44, "edu_l1_label": "IOS"}, {"txt": "2021):", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 1900000, "bbox": [[0.868, 0.727, 0.913, 0.735]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 25, "global_sentence_id": 45, "edu_l1_label": "IOS"}, {"txt": "$$L_{\\mathrm {CLIP}}=-\\frac {1}{N_{c}}\\sum _{i=1}^{N}\\log \\frac {e^{fc_{i}xfp_{y_{i}}^{T}}}{\\sum _{j=1}^{N_{p}}e^{fc_{i}xfp_{j}^{T}}}\\tag{1}$$", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2000000, "bbox": [[0.603, 0.747, 0.913, 0.799]]}]}, "tags": ["equation"], "label": "formula", "web_segment_id": 26, "global_sentence_id": 46, "edu_l1_label": "IOS"}, {"txt": "where$N_{c}$and$N_{p}$denote the number of child entities re-quiring a parent and the number of potential parent entities,respectively.The feature representations of child and parent entities are denoted as$c_{i}$and $fp_{i}$i, where $i$ is the index of the entity.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.52, 0.806, 0.913, 0.888]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 27, "global_sentence_id": 47, "edu_l1_label": "IOS"}, {"txt": "The term fpt, corresponds to the feature of the actual parent of the $i$-th child entity, where $y_{i}$ indicates its", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.52, 0.806, 0.913, 0.888]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 27, "global_sentence_id": 48, "edu_l1_label": "IOS"}, {"txt": "parent's index.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.087, 0.074, 0.189, 0.08]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 49, "edu_l1_label": "IOS"}, {"txt": "In other words, the numerator captures the similarity between a child and its correct parent, while the denominator sums over all potential parent entities.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2100000, "bbox": [[0.189, 0.074, 0.476, 0.081], [0.088, 0.088, 0.475, 0.094], [0.087, 0.1, 0.423, 0.108]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 1, "global_sentence_id": 50, "edu_l1_label": "IOS"}, {"txt": "Then we can reformulate the equation 1 as:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2200000, "bbox": [[0.105, 0.114, 0.387, 0.122]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 2, "global_sentence_id": 51, "edu_l1_label": "IOS"}, {"txt": "$$L_{\\mathrm {CLIP}}=-\\frac {1}{N_{c}}\\sum _{i=1}^{N}\\log \\frac {e^{s\\left\\|fc_{i}\\right\\|\\left\\|fp_{y_{i}}^{T}\\right\\|\\cos \\left(\\theta _{i,y_{i}}\\right)}}{\\sum _{j=1}^{N_{p}}e^{s\\left\\|fc_{i}\\right\\|\\left\\|fp_{j}^{T}\\right\\|\\cos \\left(\\theta _{i,j}\\right)}}\\tag{2}$$", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2300000, "bbox": [[0.132, 0.131, 0.48, 0.184]]}]}, "tags": ["equation"], "label": "formula", "web_segment_id": 3, "global_sentence_id": 52, "edu_l1_label": "IOS"}, {"txt": "where$\\theta _{i,j}$is the angle between the child feature vector$fc_{i}$and the parent feature vector$fp_{j}.$Ascaling factor $S$ is then introduced to restore the magnitude after normalization.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2400000, "bbox": [[0.086, 0.189, 0.48, 0.229]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 4, "global_sentence_id": 53, "edu_l1_label": "IOS"}, {"txt": "Following NormFace (Wang et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.086, 0.232, 0.48, 0.328]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 54, "edu_l1_label": "IOS"}, {"txt": "2017), we normalize the feature vectors so that$\\left\\|fc_{i}\\right\\|=\\left\\|fp_{j}\\right\\|=1$for all $i,j$.Building upon ArcFace (Deng et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.086, 0.232, 0.48, 0.328]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 55, "edu_l1_label": "IOS"}, {"txt": "2019), we further in-troduce a margin parameter $m$, which strengthens the re-lationship between parent-child pairs while increasing the separation between unrelated entities.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.086, 0.232, 0.48, 0.328]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 56, "edu_l1_label": "IOS"}, {"txt": "By this way, the loss function is rewritten as:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2500000, "bbox": [[0.086, 0.232, 0.48, 0.328]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 5, "global_sentence_id": 57, "edu_l1_label": "IOS"}, {"txt": "$$L_{\\text {matching}}=-\\frac {1}{N_{c}}\\sum _{i=1}^{N}\\log \\frac {e^{sx\\cos \\left(\\theta _{i,y_{i}}+m\\right)}}{e^{sx\\cos \\left(\\theta _{i,y_{i}}+m\\right)}+\\sum _{j=1,j\\neq i}^{N_{p}}e^{sx\\cos \\left(\\theta _{i,j}\\right)}}$$", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2600000, "bbox": [[0.087, 0.343, 0.479, 0.398]]}]}, "tags": ["equation"], "label": "formula", "web_segment_id": 6, "global_sentence_id": 58, "edu_l1_label": "IOS"}, {"txt": "(3)", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2600000, "bbox": [[0.462, 0.397, 0.478, 0.407]]}]}, "tags": ["text"], "label": "formula", "web_segment_id": 7, "global_sentence_id": 59, "edu_l1_label": "IOS"}, {"txt": "Thus, we effectively incorporate advancements in large margin cosine loss into feature matchinglearning.With this loss function, the feature extraction process is optimized such that, even with the margin parameter $m$,features of re-lated parent-child entities remain closer in the learned space compared to unrelated ones.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.085, 0.41, 0.48, 0.518]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 60, "edu_l1_label": "IOS"}, {"txt": "This encourages the model to form more discriminative representations, enhancing the ac-curacy of hierarchical relationship inference.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2700000, "bbox": [[0.085, 0.41, 0.48, 0.518]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 8, "global_sentence_id": 61, "edu_l1_label": "IOS"}, {"txt": "Greedy algorithms", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2800000, "bbox": [[0.087, 0.532, 0.23, 0.542]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 9, "global_sentence_id": 62, "edu_l1_label": "BOS"}, {"txt": "In addition to the loss function introduced earlier, we identified several structural patterns that improve both the accuracy and computational efficiency of our approach.These heuristics allow for a more efficient assignment of parent-child relationships, reducing the need for exhaustive pairwise comparisons.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.087, 0.551, 0.477, 0.559], [0.087, 0.565, 0.476, 0.572], [0.087, 0.581, 0.48, 0.592], [0.088, 0.592, 0.478, 0.6], [0.087, 0.608, 0.477, 0.614], [0.087, 0.621, 0.239, 0.628]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 63, "edu_l1_label": "IOS"}, {"txt": "First, entities belonging to specific categories never have a parent, including: abstract,appendix_list, cross, figure, form_title,list_of_figures, list_of_tables, other,references, report_title, section, summary,table,table_of_contents,andtitle.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 2900000, "bbox": [[0.239, 0.621, 0.478, 0.628], [0.087, 0.636, 0.478, 0.644], [0.088, 0.65, 0.48, 0.657], [0.088, 0.661, 0.478, 0.671], [0.088, 0.677, 0.479, 0.686], [0.088, 0.691, 0.393, 0.698]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 10, "global_sentence_id": 64, "edu_l1_label": "IOS"}, {"txt": "Second, a strong hierarchical relationship exists among the categories:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.104, 0.703, 0.478, 0.711], [0.087, 0.719, 0.267, 0.725]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 11, "global_sentence_id": 65, "edu_l1_label": "IOS"}, {"txt": " section, subsection,subsubsection,    subsubsubsection,and paragraph.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.267, 0.719, 0.479, 0.727], [0.088, 0.732, 0.219, 0.74], [0.257, 0.732, 0.432, 0.74], [0.453, 0.732, 0.475, 0.739], [0.087, 0.747, 0.184, 0.753]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 11, "global_sentence_id": 66, "edu_l1_label": "IOS"}, {"txt": "These entities follow a strict sequential structure, where each entity is assigned as a child to the nearest preceding entity in the order.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3000000, "bbox": [[0.184, 0.747, 0.478, 0.753], [0.087, 0.759, 0.476, 0.766], [0.087, 0.773, 0.328, 0.78]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 11, "global_sentence_id": 67, "edu_l1_label": "IOS"}, {"txt": "Third, specific entities exhibit predefined parent-child de-pendencies:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3100000, "bbox": [[0.105, 0.785, 0.479, 0.793], [0.087, 0.802, 0.167, 0.807]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 12, "global_sentence_id": 68, "edu_l1_label": "IOS"}, {"txt": "·table_caption is always a child of table.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3200000, "bbox": [[0.094, 0.819, 0.419, 0.826]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 13, "global_sentence_id": 69, "edu_l1_label": "IOS"}, {"txt": "·figure_caption is always a child of figure.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3300000, "bbox": [[0.094, 0.833, 0.439, 0.842]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 14, "global_sentence_id": 70, "edu_l1_label": "IOS"}, {"txt": "·form is always a child of summary, abstract,section, subsection, subsubsection, or subsubsubsection.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3400000, "bbox": [[0.094, 0.848, 0.478, 0.86], [0.109, 0.866, 0.476, 0.872], [0.109, 0.88, 0.269, 0.887]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 15, "global_sentence_id": 71, "edu_l1_label": "IOS"}, {"txt": "·list is a child of paragraph, section,subsection,       subsubsection,       or subsubsubsection.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3500000, "bbox": [[0.528, 0.074, 0.913, 0.082], [0.543, 0.088, 0.645, 0.096], [0.706, 0.088, 0.837, 0.096], [0.897, 0.088, 0.911, 0.095], [0.542, 0.102, 0.703, 0.108]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 16, "global_sentence_id": 72, "edu_l1_label": "IOS"}, {"txt": "·form_body is a child of form_title,summary, abstract, section, subsection,subsubsection, or subsubsubsection.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3600000, "bbox": [[0.527, 0.117, 0.913, 0.126], [0.543, 0.132, 0.913, 0.14], [0.543, 0.145, 0.855, 0.153]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 17, "global_sentence_id": 73, "edu_l1_label": "IOS"}, {"txt": "By enforcing these structured dependencies, we further im-prove the accuracy of entity relationship prediction while maintaining computational efficiency.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3700000, "bbox": [[0.521, 0.161, 0.91, 0.17], [0.521, 0.177, 0.911, 0.184], [0.522, 0.19, 0.77, 0.197]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 18, "global_sentence_id": 74, "edu_l1_label": "IOS"}, {"txt": "Experiments", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3800000, "bbox": [[0.663, 0.215, 0.769, 0.225]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 19, "global_sentence_id": 75, "edu_l1_label": "BOS"}, {"txt": "In this section, we present the competition dataset, our im-plementation, and the archived results of the competition.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 3900000, "bbox": [[0.521, 0.234, 0.911, 0.242], [0.521, 0.25, 0.896, 0.256]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 20, "global_sentence_id": 76, "edu_l1_label": "IOS"}, {"txt": "Dataset", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4000000, "bbox": [[0.521, 0.273, 0.579, 0.282]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 21, "global_sentence_id": 77, "edu_l1_label": "IOS"}, {"txt": "The dataset contains 571 training, 165 validation, and 81test documents.Labels are provided only for the training set,while the validation and test sets are used for the public and private leaderboards.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.522, 0.291, 0.909, 0.3], [0.521, 0.306, 0.912, 0.314], [0.522, 0.319, 0.91, 0.327], [0.521, 0.335, 0.661, 0.34]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 22, "global_sentence_id": 78, "edu_l1_label": "IOS"}, {"txt": "Each document contains multiple ob-jects,each with a bounding box in the scanned document and optional text content.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.661, 0.335, 0.913, 0.338], [0.521, 0.348, 0.91, 0.355], [0.521, 0.362, 0.694, 0.368]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 22, "global_sentence_id": 79, "edu_l1_label": "IOS"}, {"txt": "The task is to predict the parent object for each object, evaluated using accuracy.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4100000, "bbox": [[0.694, 0.362, 0.913, 0.369], [0.521, 0.375, 0.838, 0.382]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 22, "global_sentence_id": 80, "edu_l1_label": "IOS"}, {"txt": "Implemtentation", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4200000, "bbox": [[0.521, 0.398, 0.649, 0.408]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 23, "global_sentence_id": 81, "edu_l1_label": "IOS"}, {"txt": "We use a pre-trained BART-large (Lewis 2019) for text fea-ture extraction and a pre-trained SwinV2-Tiny (Liu et al.2022) for image features.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.522, 0.416, 0.91, 0.426], [0.522, 0.432, 0.912, 0.439], [0.521, 0.444, 0.685, 0.453]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 24, "global_sentence_id": 82, "edu_l1_label": "IOS"}, {"txt": "The extracted features are concate-nated with additional information suchas page number and bounding box position.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.685, 0.444, 0.91, 0.453], [0.521, 0.461, 0.91, 0.467], [0.521, 0.472, 0.676, 0.48]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 24, "global_sentence_id": 83, "edu_l1_label": "IOS"}, {"txt": "Two separate encoders, each with a 512-dimensional output, are used to extract features for par-ent and child entities.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4300000, "bbox": [[0.676, 0.472, 0.911, 0.481], [0.521, 0.485, 0.912, 0.492], [0.521, 0.501, 0.662, 0.508]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 24, "global_sentence_id": 84, "edu_l1_label": "IOS"}, {"txt": "Results", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4400000, "bbox": [[0.521, 0.524, 0.577, 0.533]]}]}, "tags": ["title"], "label": "title2", "web_segment_id": 25, "global_sentence_id": 85, "edu_l1_label": "IOS"}, {"txt": "Due to time constraints in the competition, we were unable to conduct extensive experiments on alternative methods or additional datasets.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.521, 0.542, 0.911, 0.551], [0.521, 0.557, 0.911, 0.565], [0.521, 0.572, 0.653, 0.579]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 26, "global_sentence_id": 86, "edu_l1_label": "IOS"}, {"txt": "Table 1 presents the main results ob-tained on the competition dataset, evaluated on the valida-tion set and test set.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4500000, "bbox": [[0.653, 0.572, 0.911, 0.576], [0.521, 0.585, 0.912, 0.59], [0.521, 0.599, 0.65, 0.607]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 26, "global_sentence_id": 87, "edu_l1_label": "IOS"}, {"txt": "table", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4600000, "bbox": [[0.596, 0.619, 0.841, 0.678]]}]}, "tags": ["table"], "label": "table", "web_segment_id": 27, "global_sentence_id": 88, "edu_l1_label": "EDU_O"}, {"txt": "Table 1: Performance comparison of our methods.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4700000, "bbox": [[0.55, 0.686, 0.882, 0.703]]}]}, "tags": ["table_caption"], "label": "table_title", "web_segment_id": 28, "global_sentence_id": 89, "edu_l1_label": "EDU_O"}, {"txt": "Conclusion", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4800000, "bbox": [[0.669, 0.734, 0.762, 0.745]]}]}, "tags": ["title"], "label": "title1", "web_segment_id": 29, "global_sentence_id": 90, "edu_l1_label": "BOS"}, {"txt": "In this work, we proposed a novel approach for docu-ment structure parsing, combining large margin loss with heuristic-based greedy algorithms.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.521, 0.753, 0.91, 0.762], [0.522, 0.77, 0.909, 0.776], [0.522, 0.781, 0.757, 0.789]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 30, "global_sentence_id": 91, "edu_l1_label": "IOS"}, {"txt": "Our method enhances feature discrimination while efficiently capturing hierar-chical relationships.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.757, 0.781, 0.911, 0.79], [0.522, 0.795, 0.912, 0.801], [0.521, 0.809, 0.656, 0.816]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 30, "global_sentence_id": 92, "edu_l1_label": "IOS"}, {"txt": "The results demonstrate the effective-ness of integrating deep learning with rule-based refine-ments, achieving state-of-the-art performance in the AAAI-25 VRD-IU challenge.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.656, 0.809, 0.91, 0.816], [0.522, 0.824, 0.912, 0.828], [0.522, 0.838, 0.912, 0.842], [0.521, 0.849, 0.672, 0.859]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 30, "global_sentence_id": 93, "edu_l1_label": "IOS"}, {"txt": "Future work includes exploring more diverse datasets and refining heuristic rules for better gener-alization.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 4900000, "bbox": [[0.672, 0.849, 0.911, 0.859], [0.521, 0.864, 0.913, 0.869], [0.521, 0.879, 0.583, 0.886]]}]}, "tags": ["text"], "label": "content", "web_segment_id": 30, "global_sentence_id": 94, "edu_l1_label": "IOS"}, {"txt": "References", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5000000, "bbox": [[0.237, 0.069, 0.328, 0.081]]}]}, "tags": ["title"], "label": "reference", "web_segment_id": 1, "global_sentence_id": 95, "edu_l1_label": "EDU_O"}, {"txt": "Deng,J.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.087, 0.089, 0.15, 0.099]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 2, "global_sentence_id": 96, "edu_l1_label": "EDU_O"}, {"txt": "Guo,J.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.15, 0.089, 0.202, 0.098]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 2, "global_sentence_id": 97, "edu_l1_label": "EDU_O"}, {"txt": "; Xue, N.; and Zafeiriou, S. 2019.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.202, 0.089, 0.418, 0.098]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 2, "global_sentence_id": 98, "edu_l1_label": "EDU_O"}, {"txt": "Arcface:Additive angular margin loss for deep face recognition.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.418, 0.089, 0.479, 0.098], [0.087, 0.104, 0.458, 0.112]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 2, "global_sentence_id": 99, "edu_l1_label": "EDU_O"}, {"txt": "In Proceedings of the IEEE/CVF conference on computer vi-sion and pattern recognition, 4690-4699.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5100000, "bbox": [[0.458, 0.104, 0.476, 0.112], [0.088, 0.117, 0.479, 0.124], [0.087, 0.133, 0.36, 0.14]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 2, "global_sentence_id": 100, "edu_l1_label": "EDU_O"}, {"txt": "Ding, Y.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5200000, "bbox": [[0.087, 0.147, 0.15, 0.157]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 3, "global_sentence_id": 101, "edu_l1_label": "EDU_O"}, {"txt": " Lee, J.; and Han, S. C.2024.Deep Learning based Visually Rich Document Content Understanding:", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5200000, "bbox": [[0.15, 0.147, 0.478, 0.156], [0.096, 0.161, 0.412, 0.17]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 3, "global_sentence_id": 102, "edu_l1_label": "EDU_O"}, {"txt": " A Survey.arXiv preprint arXiv:2408.01287.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5200000, "bbox": [[0.412, 0.161, 0.48, 0.17], [0.087, 0.178, 0.308, 0.184]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 3, "global_sentence_id": 103, "edu_l1_label": "EDU_O"}, {"txt": "Duong, A.-K.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5300000, "bbox": [[0.087, 0.193, 0.19, 0.202]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 4, "global_sentence_id": 104, "edu_l1_label": "EDU_O"}, {"txt": " and Gomez-Krämer, P. 2025a.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5300000, "bbox": [[0.19, 0.193, 0.405, 0.201]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 4, "global_sentence_id": 105, "edu_l1_label": "EDU_O"}, {"txt": "Address-ing Out-of-Label Hazard Detection in Dashcam Videos:Insights from the COOOL Challenge.arXiv preprint arXiv:2501.16037.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5300000, "bbox": [[0.405, 0.193, 0.479, 0.199], [0.088, 0.208, 0.478, 0.215], [0.087, 0.22, 0.366, 0.229], [0.378, 0.223, 0.479, 0.229], [0.087, 0.236, 0.214, 0.243]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 4, "global_sentence_id": 106, "edu_l1_label": "EDU_O"}, {"txt": "Duong, A.-K.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5400000, "bbox": [[0.088, 0.251, 0.189, 0.261]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 5, "global_sentence_id": 107, "edu_l1_label": "EDU_O"}, {"txt": " and Gomez-Krämer, P. 2025b.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5400000, "bbox": [[0.189, 0.251, 0.408, 0.26]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 5, "global_sentence_id": 108, "edu_l1_label": "EDU_O"}, {"txt": "Scalable Framework for Classifying AI-Generated Content Across Modalities.arXiv preprint arXiv:2502.00375.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5400000, "bbox": [[0.408, 0.251, 0.478, 0.259], [0.088, 0.265, 0.478, 0.273], [0.087, 0.279, 0.389, 0.288]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 5, "global_sentence_id": 109, "edu_l1_label": "EDU_O"}, {"txt": "Duong, A.-K.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5500000, "bbox": [[0.088, 0.295, 0.19, 0.306]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 6, "global_sentence_id": 110, "edu_l1_label": "EDU_O"}, {"txt": " Nguyen, H.-L.; and Truong, T.-T. 2022.Large margin cotangent loss for deep similarity learning.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5500000, "bbox": [[0.19, 0.295, 0.48, 0.304], [0.088, 0.31, 0.462, 0.318]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 6, "global_sentence_id": 111, "edu_l1_label": "EDU_O"}, {"txt": "In 2022 International Conference on Advanced Computing and Analytics (ACOMPA), 40-47.IEEE.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5500000, "bbox": [[0.462, 0.31, 0.476, 0.318], [0.087, 0.323, 0.477, 0.332], [0.087, 0.337, 0.323, 0.345]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 6, "global_sentence_id": 112, "edu_l1_label": "EDU_O"}, {"txt": "Hu, K.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.095, 0.354, 0.14, 0.364]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 7, "global_sentence_id": 113, "edu_l1_label": "EDU_O"}, {"txt": " Wu, Z.; Zhong, Z.; Lin, W.; Sun, L.; and Huo, Q.2023.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.14, 0.354, 0.48, 0.362], [0.087, 0.367, 0.128, 0.377]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 7, "global_sentence_id": 114, "edu_l1_label": "EDU_O"}, {"txt": "A question-answering approach to key value pair ex-traction from form-like document images.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.128, 0.367, 0.479, 0.374], [0.087, 0.384, 0.36, 0.39]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 7, "global_sentence_id": 115, "edu_l1_label": "EDU_O"}, {"txt": "In Proceedings of the AAAI Conference on ArtificialIntelligence,volume 37,12899-12906.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5600000, "bbox": [[0.36, 0.384, 0.479, 0.391], [0.088, 0.396, 0.48, 0.405], [0.088, 0.408, 0.184, 0.419]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 7, "global_sentence_id": 116, "edu_l1_label": "EDU_O"}, {"txt": "Lewis, M. 2019.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5700000, "bbox": [[0.087, 0.427, 0.205, 0.435]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 8, "global_sentence_id": 117, "edu_l1_label": "EDU_O"}, {"txt": "Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation,and comprehension.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5700000, "bbox": [[0.205, 0.427, 0.478, 0.436], [0.087, 0.443, 0.476, 0.449], [0.087, 0.456, 0.195, 0.463]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 8, "global_sentence_id": 118, "edu_l1_label": "EDU_O"}, {"txt": "arXiv preprint arXiv:1910.13461.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5700000, "bbox": [[0.195, 0.456, 0.418, 0.463]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 8, "global_sentence_id": 119, "edu_l1_label": "EDU_O"}, {"txt": "Li, M.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.087, 0.472, 0.136, 0.481]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 9, "global_sentence_id": 120, "edu_l1_label": "EDU_O"}, {"txt": " Xu, Y.; Cui, L.; Huang, S.; Wei, F.; Li, Z.; and Zhou,M.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.136, 0.472, 0.48, 0.481], [0.087, 0.485, 0.109, 0.494]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 9, "global_sentence_id": 121, "edu_l1_label": "EDU_O"}, {"txt": "2020.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.109, 0.485, 0.15, 0.494]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 9, "global_sentence_id": 122, "edu_l1_label": "EDU_O"}, {"txt": "DocBank: A benchmark dataset for document lay-out analysis.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.15, 0.485, 0.479, 0.491], [0.087, 0.501, 0.171, 0.507]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 9, "global_sentence_id": 123, "edu_l1_label": "EDU_O"}, {"txt": "arXiv preprint arXiv:2006.01038.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5800000, "bbox": [[0.171, 0.501, 0.397, 0.508]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 9, "global_sentence_id": 124, "edu_l1_label": "EDU_O"}, {"txt": "Liu,Z.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.087, 0.516, 0.143, 0.525]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 125, "edu_l1_label": "EDU_O"}, {"txt": "Hu,H.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.143, 0.516, 0.19, 0.524]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 126, "edu_l1_label": "EDU_O"}, {"txt": "; Lin,Y.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.19, 0.516, 0.248, 0.524]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 127, "edu_l1_label": "EDU_O"}, {"txt": "; Yao, Z.;Xie,Z.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.248, 0.516, 0.36, 0.523]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 128, "edu_l1_label": "EDU_O"}, {"txt": "; Wei,Y.;Ning,J.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.36, 0.516, 0.476, 0.525]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 129, "edu_l1_label": "EDU_O"}, {"txt": ";Cao, Y.; Zhang, Z.; Dong, L.; et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.476, 0.516, 0.479, 0.525], [0.087, 0.53, 0.319, 0.538]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 130, "edu_l1_label": "EDU_O"}, {"txt": "2022.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.319, 0.53, 0.36, 0.539]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 131, "edu_l1_label": "EDU_O"}, {"txt": "Swin transformer v2: Scaling up capacity and resolution.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.36, 0.53, 0.478, 0.539], [0.087, 0.545, 0.352, 0.552]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 132, "edu_l1_label": "EDU_O"}, {"txt": "In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,12009-12019.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 5900000, "bbox": [[0.352, 0.545, 0.479, 0.554], [0.088, 0.559, 0.476, 0.566], [0.087, 0.574, 0.266, 0.58]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 10, "global_sentence_id": 133, "edu_l1_label": "EDU_O"}, {"txt": "Radford, A.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.087, 0.588, 0.173, 0.598]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 134, "edu_l1_label": "EDU_O"}, {"txt": " Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh,G.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.173, 0.588, 0.478, 0.598]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 135, "edu_l1_label": "EDU_O"}, {"txt": ";Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.;Clark,J.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.478, 0.588, 0.48, 0.598], [0.088, 0.603, 0.475, 0.612]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 136, "edu_l1_label": "EDU_O"}, {"txt": ";et al.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.475, 0.603, 0.479, 0.612], [0.087, 0.618, 0.123, 0.625]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 137, "edu_l1_label": "EDU_O"}, {"txt": "2021.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.123, 0.618, 0.166, 0.625]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 138, "edu_l1_label": "EDU_O"}, {"txt": "Learning transferable visual models from nat-ural language supervision.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.166, 0.618, 0.479, 0.622], [0.087, 0.632, 0.266, 0.638]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 139, "edu_l1_label": "EDU_O"}, {"txt": "In International conference on machine learning, 8748-8763.PMLR.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6000000, "bbox": [[0.266, 0.632, 0.475, 0.639], [0.087, 0.646, 0.342, 0.652]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 11, "global_sentence_id": 140, "edu_l1_label": "EDU_O"}, {"txt": "Wang,F.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6100000, "bbox": [[0.088, 0.66, 0.153, 0.67]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 12, "global_sentence_id": 141, "edu_l1_label": "EDU_O"}, {"txt": "; Xiang,X.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6100000, "bbox": [[0.153, 0.66, 0.233, 0.669]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 12, "global_sentence_id": 142, "edu_l1_label": "EDU_O"}, {"txt": ";Cheng, J.; and Yuille, A. L. 2017.Normface: L2 hypersphere embedding for face verification.In Proceedings of the 25th ACM international conference on Multimedia,1041-1049.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6100000, "bbox": [[0.233, 0.66, 0.48, 0.669], [0.088, 0.674, 0.48, 0.682], [0.087, 0.688, 0.475, 0.697], [0.087, 0.701, 0.251, 0.711]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 12, "global_sentence_id": 143, "edu_l1_label": "EDU_O"}, {"txt": "Wang, Z.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6200000, "bbox": [[0.087, 0.718, 0.157, 0.729]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 13, "global_sentence_id": 144, "edu_l1_label": "EDU_O"}, {"txt": " Zhan, M.; Liu, X.; and Liang, D. 2020.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6200000, "bbox": [[0.157, 0.718, 0.436, 0.727]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 13, "global_sentence_id": 145, "edu_l1_label": "EDU_O"}, {"txt": "Doc-struct: A multimodal method to extract hierarchy structure in document for general form understanding.arXiv preprint arXiv:2010.11685.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6200000, "bbox": [[0.436, 0.718, 0.475, 0.727], [0.087, 0.735, 0.476, 0.742], [0.087, 0.749, 0.478, 0.756], [0.087, 0.763, 0.212, 0.77]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 13, "global_sentence_id": 146, "edu_l1_label": "EDU_O"}, {"txt": "Xu, Y.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6300000, "bbox": [[0.087, 0.778, 0.138, 0.791]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 14, "global_sentence_id": 147, "edu_l1_label": "EDU_O"}, {"txt": " Li, M.; Cui, L.; Huang, S.; Wei, F.; and Zhou,M.2020.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6300000, "bbox": [[0.138, 0.778, 0.479, 0.787], [0.087, 0.791, 0.128, 0.799]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 14, "global_sentence_id": 148, "edu_l1_label": "EDU_O"}, {"txt": "Layoutlm: Pre-training of text and layout for docu-ment image understanding.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6300000, "bbox": [[0.128, 0.791, 0.478, 0.797], [0.088, 0.808, 0.266, 0.814]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 14, "global_sentence_id": 149, "edu_l1_label": "EDU_O"}, {"txt": "In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining,1192-1200.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6300000, "bbox": [[0.266, 0.808, 0.475, 0.814], [0.088, 0.819, 0.478, 0.83], [0.089, 0.833, 0.269, 0.841]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 14, "global_sentence_id": 150, "edu_l1_label": "EDU_O"}, {"txt": "Zhang,Y.;", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.087, 0.85, 0.163, 0.859]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 151, "edu_l1_label": "EDU_O"}, {"txt": "Zhang,B.;Wang,R.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.163, 0.85, 0.307, 0.859]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 152, "edu_l1_label": "EDU_O"}, {"txt": "; Cao, J.;Li,C.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.307, 0.85, 0.413, 0.859]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 153, "edu_l1_label": "EDU_O"}, {"txt": ";and Bao,Z.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.413, 0.85, 0.478, 0.859], [0.088, 0.863, 0.105, 0.872]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 154, "edu_l1_label": "EDU_O"}, {"txt": "2021.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.105, 0.863, 0.145, 0.872]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 155, "edu_l1_label": "EDU_O"}, {"txt": "Entity relation extraction as dependency parsing in visually rich documents.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.145, 0.863, 0.478, 0.872], [0.087, 0.88, 0.25, 0.886]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 156, "edu_l1_label": "EDU_O"}, {"txt": "arXiv preprint arXiv:2110.09915.", "language": "english", "position": {"pdf_position": [{"page_number": 0, "shape": [1650, 1275], "position_id": 6400000, "bbox": [[0.25, 0.88, 0.476, 0.886]]}]}, "tags": ["text"], "label": "reference", "web_segment_id": 15, "global_sentence_id": 157, "edu_l1_label": "EDU_O"}], "type": "PDF"}
# Hierarchical Document Parsing via Large Margin Feature Matching andHeuristics
## Abstract
## Introduction
## Related Work
## Method
### Margin loss for matching
### Greedy algorithms
## Experiments
### Dataset
### Implemtentation
### Results
## Conclusion
