PVIT
Visual Instruction Model
A project that extends large language models by integrating an additional region-level vision encoder to improve visual instruction tuning.
Repository of paper: Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
37 stars
2 watching
2 forks
Language: Python
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| A dataset and model designed to scale visual instruction tuning using language-only GPT-4 models. | 164 |
| A method and toolkit for fine-tuning large language models to perform visual instruction tasks in multiple languages. | 34 |
| A multimodal benchmark dataset designed to evaluate the performance of vision-language foundation models through instruction tuning. | 134 |
| Transfers visual prompt generators across large language models to reduce training costs and enable customization of multimodal LLMs | 270 |
| A system designed to enable large multimodal models to understand arbitrary visual prompts | 302 |
| An open-source project that enhances visual instruction tuning for text-rich image understanding by integrating GPT-4 models with multimodal datasets. | 259 |
| An implementation of Pyramid Vision Transformers for image classification, object detection, and semantic segmentation tasks | 1,745 |
| A dataset of fine-grained visual instructions generated by prompting a large language model with images from another dataset | 131 |
| Creating synthetic visual reasoning instructions to improve the performance of large language models on image-related tasks | 18 |
| An annotated preference dataset and training framework for improving large vision language models. | 88 |
| A unified framework for training large language models to understand and generate visual content | 544 |
| Training and deploying large language models on computer vision tasks using region-of-interest inputs | 517 |
| Autonomously generates high-quality image-text instruction fine-tuning datasets | 91 |
| A benchmark suite for evaluating the performance of video generative models | 643 |
| A Python library for simulating photovoltaic energy system performance and modeling solar energy systems. | 1,228 |