DharmaOCR models reach 0.925 and 0.911 extraction scores with 0.40% and 0.20% degeneration rates on a new benchmark covering printed, handwritten, and legal documents, outperforming open-source and commercial baselines via SFT plus DPO.
Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces VIG metric to measure visual contribution via perplexity reduction and applies it for selective training of LVLMs on high-VIG samples and tokens to improve grounding with reduced supervision.
A generative pipeline creates realistic synthetic pitting defects and other surface flaws that, when added to real training data, yield modest gains in industrial defect detectors without replacing the need for authentic samples.
citing papers explorer
-
DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines
DharmaOCR models reach 0.925 and 0.911 extraction scores with 0.40% and 0.20% degeneration rates on a new benchmark covering printed, handwritten, and legal documents, outperforming open-source and commercial baselines via SFT plus DPO.
-
Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain
Introduces VIG metric to measure visual contribution via perplexity reduction and applies it for selective training of LVLMs on high-VIG samples and tokens to improve grounding with reduced supervision.
-
SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection
A generative pipeline creates realistic synthetic pitting defects and other surface flaws that, when added to real training data, yield modest gains in industrial defect detectors without replacing the need for authentic samples.