Summary of Multimodal Autoregressive Pre-training Of Large Vision Encoders, by Enrico Fini et al.
Multimodal Autoregressive Pre-training of Large Vision Encoders
by Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor Guilherme Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander T Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua M. Susskind, Alaaeldin El-Nouby
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method, AIMV2, is a novel approach for pre-training large-scale vision encoders that can handle images and text. Building on recent advancements in autoregressive pre-training of vision models, AIMV2 uses a straightforward process to scale up performance across various downstream tasks. The key innovation is the pairing of the vision encoder with a multimodal decoder that generates raw image patches and text tokens through an autoregressive process. This approach leads to remarkable performance not only in multimodal evaluations but also in traditional vision benchmarks such as localization, grounding, and classification. Specifically, AIMV2-3B achieves 89.5% accuracy on ImageNet-1k with a frozen trunk, outperforming state-of-the-art contrastive models like CLIP and SigLIP across diverse settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AIMV2 is a new way to train big computers that can understand pictures and words together. It works by taking small parts of an image and turning them into words, and then using those words to make the computer better at understanding images. This helps the computer do many tasks well, like recognizing objects in pictures or finding specific things in an image. AIMV2 is very good at this, and it even beats other computers that are specialized for doing just one thing. |
Keywords
» Artificial intelligence » Autoregressive » Classification » Decoder » Encoder » Grounding
