Human-in-the-Loop (HITL) represents an iterative feedback mechanism where a human or a group of humans engages with an algorithmically-generated system, like those in computer vision (CV), machine learning (ML), or artificial intelligence (AI) fields.
Each time a human offers feedback, a computer vision model undergoes updates and adapts its perception of the data it processes. The more collaborative and impactful the feedback is, the faster the model can update. This leads to more precise outcomes when the model processes the datasets used in the training procedure.
Analogously, a parent nurtures a child's development by teaching them that cats make a "meow meow" sound and dogs make a "woof woof" sound. Through this repeated guidance, the child gradually discerns the difference between a cat and a dog.