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VILA: On Pre-training for Visual Language Models

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2312.07533

Summary

Most work on visual language models (VLMs) has focused on instruction tuning. The pre-training stage, where an LLM first learns to handle images and text together, has received far less systematic study. This paper addresses that gap. Starting from Llama-2, the authors build auto-regressive VLMs, in which image tokens from a ViT pass through a projector into the LLM. They then run controlled ablations on pre-training data and training protocols, with evaluation on OKVQA, TextVQA, COCO and Flickr in 0-shot and 4-shot settings. Three lessons emerge:

  • Keeping the LLM frozen gives acceptable zero-shot results but weak in-context learning.
  • Interleaved image-text corpora such as MMC4 work better than caption pairs such as COYO.
  • Adding text-only instruction data during SFT preserves text skills and also improves visual task scores.

The resulting recipe yields VILA, a model family that beats LLaVA-1.5 and other strong baselines on many benchmarks. VILA also shows multi-image reasoning and better world knowledge, and it runs on Jetson Orin. The work matters because it offers practical, evidence-based guidance for VLM pre-training.

Key points

  • The pipeline has three stages: projector warm-up on captions, visual language pre-training of the LLM and projector, and FLAN-style visual instruction tuning. A plain linear projector beat a Transformer-block projector.
  • Layer-wise embedding similarity suggests that updating the LLM aligns visual and text representations in deeper layers. This alignment tracks the gains in 4-shot accuracy.
  • Data sources were MMC4-core and COYO, each sampled to 25M images. MMC4 averages 4 images and about 122 text tokens per image, while COYO has 1 image with about 23 tokens.
  • When MMC4 is reordered so that all images come before all text, average 4-shot accuracy falls by 37.5% and 0-shot by 4.4%.
  • Full fine-tuning clearly outperforms LoRA (rank 64), e.g., 79.9 vs. 69.4 on VQAv2. It also beats a frozen LLM with a visual expert, which nearly doubles model size. Raising image resolution from 224 to 336 lifts TextVQA from 41.6% to 49.8%.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

124 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 115 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2312.07533 · arXiv 2312.07533

@misc{lin2023vila,
  title = {{VILA: On Pre-training for Visual Language Models}},
  author = {Lin, Ji and Yin, Hongxu and Ping, Wei and Lu, Yao and Molchanov, Pavlo and Tao, Andrew and Mao, Huizi and Kautz, Jan and Shoeybi, Mohammad and Han, Song},
  year = {2023},
  doi = {10.48550/arxiv.2312.07533},
  eprint = {2312.07533},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.07533}
}

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