ViP-LLaVA
[CVPR2024] ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
AI summary
Visual Prompt Model
A system designed to enable large multimodal models to understand arbitrary visual prompts
- stars
- 302
- forks
- 21
- watching
- 5
Similar projects
Found by comparing what the projects do, not just their names.
Image processor
An all-in-one demo for interactive image processing and generation
Model trainer
A platform for training and deploying large language and vision models that can use tools to perform tasks
VQA model
A multimodal LLM designed to handle text-rich visual questions
Video image processor
An image-based language model that uses large language models to generate visual and text features from videos
LLM trainer
Transfers visual prompt generators across large language models to reduce training costs and enable customization of multimodal LLMs
Visual Model
Develops a multimodal Chinese language model with visual capabilities
Visual encoder
A vision-language model that uses a query transformer to encode images as visual tokens and allows flexible choice of the number of visual tokens.
Model debiasing
Debiasing techniques to minimize hallucinations in large visual language models
Image understanding model
A large multi-modal model developed using the Llama3 language model, designed to improve image understanding capabilities.
Multimodal LLM
An implementation of a multimodal language model with capabilities for comprehension and generation
nvlabs/prismer1.3K
Vision-Language Model
A deep learning framework for training multi-modal models with vision and language capabilities.
VQA prompter
An implementation of a two-stage framework designed to prompt large language models with answer heuristics for knowledge-based visual question answering tasks.
Vision-Language Model
A PyTorch implementation of an encoder-free vision-language model that can be fine-tuned for various tasks and modalities
Vision-Language Model
A multimodal AI model that enables real-world vision-language understanding applications
lxtgh/omg-seg1.3K
Visual Model
Develops an end-to-end model for multiple visual perception and reasoning tasks using a single encoder, decoder, and large language model.
