ImageInWords

ImageInWords (IIW) is a human-in-the-loop annotation framework designed to curate hyper-detailed image descriptions, generating a new dataset. This dataset achieves state-of-the-art results by evaluating automation and human-parallel metrics. IIW data significantly improves in multiple dimensions over previous datasets and GPT-4V outputs, including readability, comprehensiveness, specificity, hallucination, and human-likeness when generating descriptions.

Target Audience:

Researchers and developers for improving visual language models.

Educational field as a teaching tool to help students understand the relationship between images and language.

Commercial applications for creating engaging product descriptions in advertising and marketing.

Artistic creation to assist artists with inspiration and description.

Usage Scenarios:

Automatically generate detailed image descriptions in image annotation tasks.

Train chatbots to describe image content more accurately.

Provide detailed verbal descriptions of images for visually impaired individuals in assistive technology.

Product Features:

Generate hyper-detailed image descriptions for training visual language models.

Enhance dataset quality through a human-in-the-loop annotation framework.

Improve description quality and accuracy across multiple dimensions.

Support text-to-image generation tasks, producing more accurate images.

Increase accuracy in visual language combination reasoning tasks.

Offer richer and finer content descriptions.

Usage Instructions:

Step 1: Download and install necessary software and libraries.

Step 2: Download the IIW dataset from GitHub or Hugging Face.

Step 3: Use the IIW dataset to train or fine-tune visual language models.

Step 4: Utilize the trained model to generate image descriptions or perform other related tasks.

Step 5: Evaluate the quality of generated descriptions, such as accuracy and comprehensiveness.

Step 6: Adjust model parameters as needed to optimize the effect of description generation.