PaliGemma: A versatile 3B VLM for transfer

• URL: http://arxiv.org/abs/2407.07726v2
• Date: Thu, 10 Oct 2024 17:28:23 GMT
• Title: PaliGemma: A versatile 3B VLM for transfer
• Abstract summary: PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. We evaluate PaliGemma on almost 40 diverse tasks including standard VLM benchmarks, but also more specialized tasks such as remote-sensing and segmentation.
• Score: 112.41933621495446
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• FabriVLA: A Lightweight Vision-Language-Action Model for Precise Multi-Task Manipulation [5.328444469283032]
  We present FabriVLA, a lightweight Vision-Language-Action model for Precise Multi-Task Manipulation.<n>On the Meta-World MT50 benchmark spanning 50 diverse manipulation tasks, FabriVLA achieves a tier-average success rate of 90.0%.
  arXiv  Detail & Related papers  (2026-07-09T15:06:43Z)
• GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning [116.80874591718323]
  We present GLM-4.1V-Thinking and GLM-4.5V, a family of vision-language models (VLMs)<n>GLM-4.5V achieves state-of-the-art performance on nearly all tasks among open-source models of similar size.<n>The smaller GLM-4.1V-9B-Thinking remains highly competitive-achieving superior results to the much larger Qwen2.5-VL-72B on 29 benchmarks.
  arXiv  Detail & Related papers  (2025-07-01T17:55:04Z)
• EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents [63.43699771428243]
  EmbodiedBench is an extensive benchmark designed to evaluate vision-driven embodied agents. We evaluated 19 leading proprietary and open-source MLLMs within EmbodiedBench. MLLMs excel at high-level tasks but struggle with low-level manipulation.
  arXiv  Detail & Related papers  (2025-02-13T18:11:34Z)
• PaliGemma 2: A Family of Versatile VLMs for Transfer [48.68777561571185]
  PaliGemma 2 is an upgrade of the PaliGemma open Vision-Language Model (VLM) based on the Gemma 2 family of language models. We combine the SigLIP-So400m vision encoder that was also used by PaliGemma with the whole range of Gemma 2 models, from the 2B one all the way up to the 27B model. We train these models at three resolutions (224px, 448px, and 896px) in multiple stages to equip them with broad knowledge for transfer via fine-tuning.
  arXiv  Detail & Related papers  (2024-12-04T18:50:42Z)
• Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance [78.48606021719206]
  Mini-InternVL is a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. We develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:58:20Z)
• MoME: Mixture of Multimodal Experts for Generalist Multimodal Large Language Models [57.091523832149655]
  We propose a mixture of multimodal experts (MoME) to mitigate task interference and obtain a generalist MLLM. Our MoME is composed of two key components, a mixture of vision experts (MoVE) and a mixture of language experts (MoLE)
  arXiv  Detail & Related papers  (2024-07-17T16:31:38Z)
• Dense Connector for MLLMs [89.50595155217108]
  We introduce the Dense Connector - a plug-and-play vision-language connector that significantly enhances existing MLLMs. Building on this, we also propose the Efficient Dense Connector, which achieves performance comparable to LLaVA-v1.5 with only 25% of the visual tokens. Our model, trained solely on images, showcases remarkable zero-shot capabilities in video understanding as well.
  arXiv  Detail & Related papers  (2024-05-22T16:25:03Z)
• MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI [71.53579367538725]
  MMT-Bench is a benchmark designed to assess Large Vision-Language Models (LVLMs) across massive multimodal tasks. MMT-Bench comprises $31,325$ meticulously curated multi-choice visual questions from various multimodal scenarios.
  arXiv  Detail & Related papers  (2024-04-24T17:37:05Z)
• Mipha: A Comprehensive Overhaul of Multimodal Assistant with Small Language Models [25.724995114710165]
  We investigate the design aspects of Multimodal Small Language Models (MSLMs) and propose an efficient multimodal assistant named Mipha. Our Mipha-3B outperforms the state-of-the-art large MLLMs, especially LLaVA-1.5-13B, on multiple benchmarks.
  arXiv  Detail & Related papers  (2024-03-10T12:43:27Z)
• InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks [92.03764152132315]
  We design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks. It has powerful visual capabilities and can be a good alternative to the ViT-22B.
  arXiv  Detail & Related papers  (2023-12-21T18:59:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.