Exploring the Necessity of Annotation in Multi-Modal LLM Fine-Tuning for Enhanced Image Comprehension

[hdstockimages]

Is the process of annotation imperative for multi-modal large language models (LLMs) to acquire nuanced understanding of image details, or can it be deemed merely advantageous?

For instance, when I present an image and query specifics such as the presence of a speed sign on the left, children enjoying ice cream on the right, a stork in flight above, sunny weather conditions, and the asphalt pavement beneath, must I meticulously annotate these elements for the model to effectively assimilate them?

I have encountered numerous fine-tuning exemplars where only image-text pairings are employed without any form of annotation. This raises the question: does annotation serve as a critical mechanism for learning, or can models thrive on unannotated data?

[John6666]

There seems to be a post on a similar subject. Let’s join them over there.

Multi modal LLM fine tuning (image annotation)

Is Annotation Necessary for the Multi Model LLM to Learn the Details in the Image? or beneficial ? For example, when I provide an image and ask, there is a speed sign on the left side, there are children eating ice cream on the right side, the stork is flying above, the weather is sunny, the ground is asphalt and pavement, etc. Do I need to anotate these for the model to learn them? I have seen some fine tuning examples in which they only use image and text without annotation.