Summary of Paligemma: a Versatile 3b Vlm For Transfer, by Lucas Beyer et al.
PaliGemma: A versatile 3B VLM for transfer
by Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alabdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bošnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier Henaff, Xi Xiong, Radu Soricut, Jeremiah Harmsen, Xiaohua Zhai
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
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| 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 paper introduces PaliGemma, an open Vision-Language Model (VLM) that combines the SigLIP-So400m vision encoder and Gemma-2B language model. This versatile base model is trained to be effective at transferring knowledge across various tasks. It achieves strong performance on a wide range of open-world tasks, including standard VLM benchmarks and specialized tasks such as remote-sensing and segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PaliGemma is a special kind of computer program that can understand both pictures and words. It’s like a super-smart librarian who knows everything about the world! This program was created by combining two important parts: one that helps it see (SigLIP-So400m) and another that helps it talk (Gemma-2B). Because it’s so good at understanding, PaliGemma can help with lots of different tasks, from simple things like image recognition to more complex jobs like segmenting images. |
Keywords
* Artificial intelligence * Encoder * Language model
