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Team PA-VCG's Solution for Competition on Understanding Chinese College Entrance Exam Papers in ICDAR'25

arXiv 2508.00834 ↗First posted 9 Jun 2025cs.CVcited by 0

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Abstract (arXiv)

This report presents Team PA-VGG's solution for the ICDAR'25 Competition on Understanding Chinese College Entrance Exam Papers. In addition to leveraging high-resolution image processing and a multi-image end-to-end input strategy to address the challenges of dense OCR extraction and complex document layouts in Gaokao papers, our approach introduces domain-specific post-training strategies. Experimental results demonstrate that our post-training approach achieves the most outstanding performance, securing first place with an accuracy rate of 89.6%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 7 references that are in the map. First posted on arXiv 9 Jun 2025.

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References · 5

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2025Qwen2.5-VL Technical Reportcited by 5,587
  3. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,565
  4. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,079
  5. 2024Ovis: Structural Embedding Alignment for Multimodal Large Language Modelcited by 169

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