PaliGemma: A versatile 3B VLM for transfer
Authors: name: Lucas Beyer; name: Andreas Steiner; name: André Susano Pinto; name: Alexander Kolesnikov; name: Xiao Wang; name: Daniel Salz; name: Maxim Neumann; name: Ibrahim Alabdulmohsin; name: Michael Tschannen; name: Emanuele Bugliarello; name: Thomas Unterthiner; name: Daniel Keysers; name: Skanda Koppula; name: Fangyu Liu; name: Adam Grycner; name: Alexey Gritsenko; name: Neil Houlsby; name: Manoj Kumar; name: Keran Rong; name: Julian Eisenschlos; name: Rishabh Kabra; name: Matthias Bauer; name: Matko Bošnjak; name: Xi Chen; name: Matthias Minderer; name: Paul Voigtlaender; name: Ioana Bica; name: Ivana Balazevic; name: Joan Puigcerver; name: Pinelopi Papalampidi; name: Olivier Henaff; name: Xi Xiong; name: Radu Soricut; name: Jeremiah Harmsen; name: Xiaohua Zhai
Published: 2024-07-10 14:57:46

Abstract: 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.