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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.07726 |
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| _version_ | 1866913539792306176 |
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| author | Beyer, Lucas Steiner, Andreas Pinto, André Susano Kolesnikov, Alexander Wang, Xiao Salz, Daniel Neumann, Maxim Alabdulmohsin, Ibrahim Tschannen, Michael Bugliarello, Emanuele Unterthiner, Thomas Keysers, Daniel Koppula, Skanda Liu, Fangyu Grycner, Adam Gritsenko, Alexey Houlsby, Neil Kumar, Manoj Rong, Keran Eisenschlos, Julian Kabra, Rishabh Bauer, Matthias Bošnjak, Matko Chen, Xi Minderer, Matthias Voigtlaender, Paul Bica, Ioana Balazevic, Ivana Puigcerver, Joan Papalampidi, Pinelopi Henaff, Olivier Xiong, Xi Soricut, Radu Harmsen, Jeremiah Zhai, Xiaohua |
| author_facet | Beyer, Lucas Steiner, Andreas Pinto, André Susano Kolesnikov, Alexander Wang, Xiao Salz, Daniel Neumann, Maxim Alabdulmohsin, Ibrahim Tschannen, Michael Bugliarello, Emanuele Unterthiner, Thomas Keysers, Daniel Koppula, Skanda Liu, Fangyu Grycner, Adam Gritsenko, Alexey Houlsby, Neil Kumar, Manoj Rong, Keran Eisenschlos, Julian Kabra, Rishabh Bauer, Matthias Bošnjak, Matko Chen, Xi Minderer, Matthias Voigtlaender, Paul Bica, Ioana Balazevic, Ivana Puigcerver, Joan Papalampidi, Pinelopi Henaff, Olivier Xiong, Xi Soricut, Radu Harmsen, Jeremiah Zhai, Xiaohua |
| contents | PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PaliGemma: A versatile 3B VLM for transfer Beyer, Lucas Steiner, Andreas Pinto, André Susano Kolesnikov, Alexander Wang, Xiao Salz, Daniel Neumann, Maxim Alabdulmohsin, Ibrahim Tschannen, Michael Bugliarello, Emanuele Unterthiner, Thomas Keysers, Daniel Koppula, Skanda Liu, Fangyu Grycner, Adam Gritsenko, Alexey Houlsby, Neil Kumar, Manoj Rong, Keran Eisenschlos, Julian Kabra, Rishabh Bauer, Matthias Bošnjak, Matko Chen, Xi Minderer, Matthias Voigtlaender, Paul Bica, Ioana Balazevic, Ivana Puigcerver, Joan Papalampidi, Pinelopi Henaff, Olivier Xiong, Xi Soricut, Radu Harmsen, Jeremiah Zhai, Xiaohua Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation. |
| title | PaliGemma: A versatile 3B VLM for transfer |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2407.07726 |