Building and better understanding vision-language models: insights and future directions

Benchmark Model Rank Results
visual-question-answering-vqa-on-textvqa-valIdefics3-8B#20Accuracy: 74.9
visual-question-answering-on-docvqa-testIdefics3-8B#53ANLS: 0.877
vision-language-understanding-on-mmstarIdefics3-8B#88Accuracy: 55.9
multimodal-reasoning-on-mathvistaIdefics3-8B#97Accuracy: 58.4
multimodal-reasoning-on-mmmuIdefics3-8B#105Accuracy: 46.6