DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning

Benchmark Model Rank Results
visual-question-answering-vqa-on-hr-bench-4kDeepEyes#2Overall: 75.1FSP: 91.3FCP: 59.0
visual-question-answering-vqa-on-hr-bench-4kQwen2.5-VL-32B#3Overall: 73.9FSP: 89.8FCP: 58.0
visual-question-answering-vqa-on-hr-bench-4kPixel-Reasoner#4Overall: 72.9FSP: 86.0FCP: 60.3
visual-question-answering-vqa-on-hr-bench-4kQwen2.5-VL-7B#5Overall: 68.8FSP: 85.2FCP: 52.2
visual-question-answering-vqa-on-hr-bench-4kLLaVA-OneVision-7B#6Overall: 63.0FSP: 72.0FCP: 54.0
visual-question-answering-vqa-on-hr-bench-8kDeepEyes#2Overall: 72.6FSP: 86.8FCP: 58.5
visual-question-answering-vqa-on-hr-bench-8kQwen2.5-VL-32B#3Overall: 70.4FSP: 84.5FCP: 56.3
visual-question-answering-vqa-on-hr-bench-8kPixel-Reasoner#4Overall: 66.9FSP: 80.0FCP: 54.3
visual-question-answering-vqa-on-hr-bench-8kQwen2.5-VL-7B#5Overall: 65.3FSP: 78.8FCP: 51.8
visual-question-answering-vqa-on-hr-bench-8kLLaVA-OneVision-7B#6Overall: 59.8FSP: 67.3FCP: 52.3