ConsistencyDet: A Few-step Denoising Framework for Object Detection Using the Consistency Model

Understand

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology.

• In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
• This framework, termed \textbf{ConsistencyDet}, leverages an innovative denoising concept known as the Consistency Model.
• The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any time step back to its pristine state, thereby realizing a \textbf{``few-step denoising''} mechanism.