tao-generate-image-grounding

Image grounding extracts referring expressions from image-caption pairs and grounds them to pixel-space bounding boxes. Vision-Language Models automate this extraction, generating phrase-grounded annotations for each expression.

Yes, auto-labeling for phrase grounding supports Gemini, NIM, vLLM, and other OpenAI-compatible endpoints. These Vision-Language Models automate the extraction of referring expressions from image-caption pairs.

Phrase grounding generates phrase-grounded annotations for referring expressions within captions. Vision-Language Models process image-caption pairs to extract these expressions and ground them to corresponding pixel-space bounding boxes.

Yes, generating referring expression annotations requires Python and access to an image file alongside its corresponding caption file. These inputs are processed through Vision-Language Model endpoints to produce grounded annotations.

Automated phrase-grounded training data is generated by extracting referring expressions and bounding boxes from image-caption pairs. This approach is ideal for producing training data for referring-expression and grounding models.

Yes, the image grounding mechanism supports Gemini, NIM, vLLM, and other OpenAI-compatible endpoints. This allows flexible integration with various Vision-Language Models to extract referring expressions.