Research map / Vision-language and multimodal / Visual question answering
DocPrune: Efficient Document Question Answering via Background, Question, and Comprehension-aware Token Pruning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information (AAAI 2025 · similarity 0.90)
- DocVXQA: Context-Aware Visual Explanations for Document Question Answering (ICML 2025 · similarity 0.89)
- Rethinking Token Reduction for Large Vision-Language Models (CVPR 2026 · similarity 0.88)
- Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding (CVPR 2025 · similarity 0.88)
- QuietPrune: Query-Guided Early Token Pruning for Vision-Language Models (CVPR 2026 · similarity 0.87)
- Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents (CVPR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
