Language-guided Frequency Modulation for Large Vision-Language Models

Benchmark Model Rank Results
chart-question-answering-on-chartqaLFM (w/ Qwen2.5-VL)–1:1 Accuracy: 89.2
chart-question-answering-on-charxiv-dqLFM (w/ Qwen2.5-VL)–Accuracy: 75.8
chart-question-answering-on-charxiv-rqLFM (w/ Qwen2.5-VL)–Accuracy: 44.7
multimodal-reasoning-on-mathvistaLFM (w/ Qwen2.5-VL)–Accuracy: 69.8
vision-language-understanding-on-mmbench-v1-1LFM (w/ Qwen2.5-VL)–Accuracy: 84.2
vision-language-understanding-on-mmstarLFM (w/ Qwen2.5-VL)–Accuracy: 66.1
visual-question-answering-vqa-on-mmbench-cnLFM (w/ Qwen2.5-VL)–Accuracy: 84.7
visual-question-answering-vqa-on-mmbench-enLFM (w/ Qwen2.5-VL)–Accuracy: 85.0