In recent years, vision language models (VLMs) have become increasingly significant in the field of computer vision. These models bridge the gap between visual and linguistic understanding in artificial intelligence (AI), enabling a wide range of large-scale real-life applications. Consequently, systematic studies to identify the key factors in VLMs are becoming increasingly essential.
In a new paper PaliGemma: A versatile 3B VLM for transfer, a Google DeepMind research team release PaliGemma, a robust and versatile vision language model with 3 billion parameters. PaliGemma excels in transfer learning across various vision and language tasks, achieving state-of-the-art performance in a multitude of open-world applications.
The core concept behind PaliGemma is that by training on an extensive dataset, the model can learn general patterns and skills applicable to a wide range of problems. At a high level, PaliGemma functions as a VLM by taking one or more images and a textual task description (prompt or question) as input. It then autoregressively generates a prediction in the form of a text string (the answer). This straightforward image + text input, text output API is versatile enough to handle numerous standard tasks, such as image classification, captioning, visual question-answering, and dialogue.
PaliGemma’s architecture is inspired by the popular LLAVA design, utilizing a transformer-based encoder-decoder structure to process and generate both visual and textual information. It comprises three main components: an image encoder, a decoder-only language model, and a linear layer. The image encoder uses a publicly available SigLIP checkpoint, specifically the ViTSo400m image encoder. The language model employs the Gemma-2B v1.0 raw pretrained checkpoint, which balances performance and size. The linear layer projects SigLIP’s output tokens to match the dimensions of Gemma-2B’s vocabulary tokens for concatenation.
One of PaliGemma’s primary advantages is its ability to learn quickly with minimal examples. This “few-shot learning” capability makes it valuable for real-world applications where large labeled datasets may be scarce.
PaliGemma demonstrates impressive performance across a wide range of visual-language tasks. It excels in image captioning, achieving high scores on benchmarks such as COCO-Captions and TextCaps. In visual question answering, PaliGemma performs strongly on various datasets, including VQAv2, GQA, and ScienceQA. Additionally, the model shows proficiency in specialized tasks such as chart understanding (ChartQA) and OCR-related tasks (TextVQA, DocVQA).
Overall, the PaliGemma paper marks a significant contribution to the field of multimodal AI. The insights and techniques presented could pave the way for more sophisticated and capable AI systems in the future.
The paper PaliGemma: A versatile 3B VLM for transfer is on arXiv.
Author: Hecate He | Editor: Chain Zhang
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