“LLaVA-1.5: Improved Baselines With Visual Instruction Tuning”, Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee2023-10-05 (CLIP, instruct-tuning LLMs; backlinks)⁠:

Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks.

Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ~1 day on a single 8-A100 node.

We hope this can make state-of-the-art LMM research more accessible.

Code and model will be publicly available.

Backlinks: