The paper presents a new approach to visual instruction tuning by introducing a fine-grained visual instruction dataset, LVIS-INSTRUCT 4V, containing 220K visually aligned and context-aware instructions. The instructions are produced…
Continue readingThe study focuses on improving large multimodal models (LMMs), specifically the LLaVA model, through visual instruction tuning. By using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data, the…
Continue readingIn the pursuit of mastering intricate multimodal tasks, the paper introduces MM-Vet, an evaluation benchmark tailored for Large Multimodal Models (LMMs). LMMs, which enhance large language models (LLMs) with multimodal…
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