LLaVA and ViP-LLaVA
ViP-LLaVA builds upon LLaVA's architecture by extending its visual instruction tuning approach to handle arbitrary visual prompts (like spatial markers and annotations) rather than just image-text pairs, making them complementary advances in the same multimodal instruction-tuning lineage.
About LLaVA
haotian-liu/LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
LLaVA helps you understand and interact with images using natural language. You provide an image and ask questions or give instructions about its content, and it generates descriptive text, answers, or performs tasks like segmentation. This is ideal for anyone needing to extract insights from visuals, such as researchers analyzing images, content creators generating descriptions, or operations teams monitoring visual data.
About ViP-LLaVA
WisconsinAIVision/ViP-LLaVA
[CVPR2024] ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
This tool helps researchers and developers make large multimodal models (LMMs) understand specific regions or objects within an image. You provide an image and visually highlight a region (a 'visual prompt'), and the model outputs a detailed text description or answers questions about that specific area. It's designed for those working on computer vision, AI research, and multimodal AI applications.
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