PaliGemma: A versatile 3B VLM for transfer
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
2024-07-11
Summary
This paper talks about PaliGemma, a new vision-language model (VLM) that combines visual and textual understanding to perform various tasks. It is designed to be flexible and effective for many applications, achieving strong results across different types of challenges.
What's the problem?
The main problem in the field of AI is that many existing models are either too large and complex or not versatile enough to handle a wide range of tasks effectively. This limits their usability in real-world applications where different types of data and tasks are involved, such as image recognition, captioning, and answering questions about images.
What's the solution?
To address this issue, the authors developed PaliGemma, which is based on two key components: the SigLIP-So400m vision encoder for processing images and the Gemma-2B language model for understanding text. By training PaliGemma on a diverse set of tasks, the model can learn to perform well in various scenarios. It has been tested on nearly 40 different tasks, including both common benchmarks and specialized areas like remote sensing and image segmentation.
Why it matters?
This research is important because it shows that smaller models like PaliGemma can achieve high performance without needing excessive resources. By being versatile and efficient, PaliGemma can be used in a wide range of applications in AI, making it easier for developers to create systems that understand both images and text effectively. This could lead to advancements in fields such as healthcare, environmental monitoring, and education.
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.