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 perturbed bounding boxes of
annotated entities. This framework, termed 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 temporal stage back to its pristine state,
thereby realizing a ``one-step denoising'' mechanism. Such an attribute
markedly elevates the operational efficiency of the model, setting it apart
from the conventional Diffusion Model. Throughout the training phase,
ConsistencyDet initiates the diffusion sequence with noise-infused boxes
derived from the ground-truth annotations and conditions the model to perform
the denoising task. Subsequently, in the inference stage, the model employs a
denoising sampling strategy that commences with bounding boxes randomly sampled
from a normal distribution. Through iterative refinement, the model transforms
an assortment of arbitrarily generated boxes into the definitive detections.
Comprehensive evaluations employing standard benchmarks, such as MS-COCO and
LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in
performance metrics.

通过利用一种自洽模型的创新去噪概念，我们引入了一个被称为 ConsistencyDet 的新框架，用以将目标检测作为一个去噪扩散过程来表述。在训练阶段中，ConsistencyDet 使用从地面实况注释衍生的噪声注入框开始扩散序列，并调节模型执行去噪任务。综合评估表明，ConsistencyDet 在性能指标方面超越了其他领先的检测器。