blip-2-vision-language

Image captioning generates descriptive text for images by bridging frozen image encoders with large language models. You can use this Skill to automatically produce alt-text or descriptions for your photo files.

Visual question answering (VQA) answers questions about image content by connecting visual features to a language model. This Skill enables zero-shot VQA, letting you query product photos or scenes without task-specific training.

Yes, image-text retrieval requires PyTorch, transformers, and Pillow installed for image processing and model inference. These dependencies power the underlying vision-language model for state-of-the-art zero-shot performance.

Yes, generating alt-text for website images is a core use case for this image captioning Skill. It processes visual inputs using Pillow and outputs descriptive text to make your web images accessible.

Frozen image encoders limit fine-tuning flexibility but enable efficient zero-shot performance for image captioning and VQA. This approach bridges visual features to language models without updating the encoder weights.

Image-text retrieval lets you build a searchable image database by matching textual descriptions to image content. This Skill performs zero-shot retrieval using transformers and PyTorch for state-of-the-art matching.