BLIP Radiology Model

CLIP is a vision-language model developed by OpenAI that maps images and text into a shared embedding space through contrastive learning, supporting zero-shot image classification.

CLIP is a multimodal model developed by OpenAI that can understand the relationship between images and text, supporting zero-shot image classification tasks.

SigLIP is a vision-language model pre-trained on the WebLi dataset, employing an improved sigmoid loss function to optimize image-text matching tasks.

CLIP is a multimodal model developed by OpenAI that maps images and text into a shared embedding space through contrastive learning, enabling zero-shot image classification capabilities.

BLIP is an advanced vision-language pretrained model, excelling in image captioning tasks and supporting both conditional and unconditional text generation.

BLIP is a unified vision-language pretraining framework, excelling at image caption generation tasks, supporting both conditional and unconditional image caption generation.

OpenVLA 7B is an open-source vision-language-action model trained on the Open X-Embodiment dataset, capable of generating robot actions based on language instructions and camera images.

LLaVA is an open-source multimodal chatbot, fine-tuned based on LLaMA/Vicuna, supporting image-text interaction.

This is an image captioning model based on ViT and GPT2 architectures, capable of generating natural language descriptions for input images.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation tasks.