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. 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 definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://anonymous.4open.science/r/ConsistencyDet-37D5.

翻译：目标检测作为感知计算领域的核心任务，可采用生成式方法进行求解。本研究提出一种新颖框架，将目标检测表述为针对标注实体扰动边界框的去噪扩散过程。该框架名为 **ConsistencyDet**，其核心创新在于采用了一种称为一致性模型的去噪范式。该模型具备自一致性特性，能够将任意时间步的失真信息映射回原始状态，从而实现 **"少步去噪"** 机制。这一特性显著提升了模型运行效率，与传统扩散模型形成鲜明区别。在训练阶段，ConsistencyDet 从真实标注添加噪声的边界框开始扩散序列，并通过条件约束训练模型执行去噪任务。在推理阶段，模型采用从正态分布随机采样的边界框作为起点，通过迭代优化将随机生成的候选框逐步精炼为最终检测结果。基于 MS-COCO 和 LVIS 等标准基准的全面实验表明，ConsistencyDet 在性能指标上超越了其他前沿检测器。我们的代码公开于 https://anonymous.4open.science/r/ConsistencyDet-37D5。