Abstract
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.
Abstract (translated)
对象检测，在感知计算领域中是一个基本任务，可以使用生成方法来解决。在本文研究中，我们引入了一个新的框架，将对象检测视为去噪扩散过程，该过程对注释实体的扰动边界框进行操作。这个框架被称为ConsistencyDet，利用了一种创新的去噪概念，称为一致性模型。这个模型的特点是其自一致性特征，它使模型能够将来自任何时间阶段的扭曲信息映射回其原始状态，从而实现了一项“一步去噪”机制。这种属性显著提高了模型的操作效率，使它与传统的扩散模型区别开来。在训练阶段，ConsistencyDet通过从真实注释中引入噪声注入的边界框启动扩散序列，并调整模型以执行去噪任务。随后，在推理阶段，模型采用一种以从正态分布中随机采样边界框的的去噪采样策略。通过迭代优化，模型将一系列任意生成的边界框转化为确定的检测结果。使用标准的基准测试，如MS-COCO和LVIS，全面评估证实了ConsistencyDet在性能指标上超越了其他前沿检测器。
URL
https://arxiv.org/abs/2404.07773
