ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
arxiv(2024)
摘要
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 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 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://github.com/Tankowa/ConsistencyDet.
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