Annotating images for training computer vision models

A system for training a multi-task computer vision machine-learning model, comprising:

• one or more annotation specialist models configured to:
• receive a plurality of images to be annotated;
• and
• generate pre-filtered annotations for the plurality of images;
• a data filtering and enhancement module configured to:
• filter the pre-filtered annotations in accordance with predefined noise criteria so as to output candidate annotations for the plurality of images;
• an iterative data refinement model configured to:
• iteratively train the multi-task computer vision machine-learning model on the plurality of images annotated by the candidate annotations;
• and
• a final annotation module configured to store the candidate annotation into the corpus as a final annotation for its associated image.

A method for computer vision, comprising:

• receiving a first image and a first multi-task prompt related to the first image;
• encoding the first image;
• extracting a first set of embeddings from the encoded first image;
• processing the first set of embeddings and the first multi-task prompt using a sequence-to-sequence architecture operating with a single set of weights and a single loss function;
• generating a first set of tokens from the processed first set of embeddings and the first multi-task prompt;
• and
• outputting a response to the first multi-task prompt based on the first set of tokens.