Bibliographic Details
| Title: |
PaliGemma: A versatile 3B VLM for transfer |
| Authors: |
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 |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning |
| More Details: |
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.
Comment: v2 adds Appendix H and I and a few citations |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2407.07726 |
| Accession Number: |
edsarx.2407.07726 |
| Database: |
arXiv |
