ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

Benchmark Model Rank Results
visual-question-answering-on-gqa-test-devViP-LLaVA-13B#2Accuracy: 70.7
object-hallucination-evaluation-on-popeViP-LLaVA-13B#13F1: 87.4
visual-question-answering-vqa-on-seed-seedbenchViP-LLaVA-13B#29Accuracy: 62.9
visual-question-answering-on-mm-vetViP-LLaVA-13B#143GPT-4 score: 34.5