DiffusionDet: Diffusion Model for Object Detection (2211.09788v2)

Abstract: We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to the output results in a progressive way. Our work possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation. The extensive experiments on the standard benchmarks show that DiffusionDet achieves favorable performance compared to previous well-established detectors. For example, DiffusionDet achieves 5.3 AP and 4.8 AP gains when evaluated with more boxes and iteration steps, under a zero-shot transfer setting from COCO to CrowdHuman. Our code is available at https://github.com/ShoufaChen/DiffusionDet.

• The paper introduces a noise-to-box paradigm that eliminates the need for predefined object priors by starting from random noisy boxes.
• It employs a diffusion process to iteratively refine bounding boxes, achieving competitive performance across COCO, LVIS, and CrowdHuman benchmarks.
• The approach offers flexible evaluation settings, dynamically adjusting box numbers and iterations to improve generalization and zero-shot transfer.

An Overview of "DiffusionDet: Diffusion Model for Object Detection"

The paper "DiffusionDet: Diffusion Model for Object Detection" introduces a novel approach to object detection by utilizing a diffusion model framework. The traditional view of object detection is reformulated as a denoising process that moves from noisy initial bounding boxes to accurate object boxes.

Core Contributions

DiffusionDet models the object detection problem using a generative framework. During training, object boxes diffuse from predefined ground truth boxes, becoming progressively more random. The model is designed to reverse this diffusion process, effectively learning to predict the true object boxes from noisy counterparts.

Several key innovations make DiffusionDet noteworthy:

1. Noise-to-Box Detection Paradigm: Unlike standard object detection pipelines that rely on fixed or empirically designed object priors (such as anchor boxes or predefined queries), DiffusionDet begins with random noisy boxes. This significantly simplifies the detection process, as it requires neither heuristic object priors nor learned queries during inference.
2. Flexibility in Evaluation: DiffusionDet offers remarkable flexibility. It supports a dynamic number of boxes during evaluation and allows iterative refinement of predictions without retraining. This flexibility is especially beneficial when transferring models across different datasets or adjusting to specific data conditions.
3. Performance Gains in Zero-Shot Transfer: When evaluated on diverse detection benchmarks, DiffusionDet exhibits strong performance. In particular, it demonstrates impressive gains when models pretrained on the COCO dataset are transferred to more crowded datasets like CrowdHuman. The flexibility to modify evaluation settings results in improved performance without additional training.

Experimental Results

DiffusionDet was tested extensively on COCO, LVIS, and CrowdHuman datasets. It consistently delivered competitive results compared to existing state-of-the-art detectors like DETR, Sparse R-CNN, and Cascade R-CNN. Notably, the model achieved significant improvements by adjusting the number of evaluation boxes and iterations, illustrating its adaptability to various scenarios.

Further, the paper showed that DiffusionDet could perform well under different conditions by decoupling the training and inference settings. The model's dynamic adjustment capability is highlighted as a significant advantage, providing practical benefits depending on the computational budget or specific application requirements.

Technical Highlights

• Inference Flexibility: The framework supports modifications in the number of random boxes and iterative steps during inference, allowing a single model to adapt to a wide range of operational scenarios.
• Integration of Diffusion Models: This is one of the first applications of diffusion models to object detection, merging generative model techniques with perception tasks.
• Robust Generalization: The experiments underscore the model's ability to generalize across datasets without degradation in performance, a feature not commonly found in typical object detection frameworks.

Implications and Future Directions

The introduction of a diffusion-based model for object detection opens several new avenues for research. It provides a bridge between generative modeling and perceptual tasks in computer vision, potentially inspiring further developments in hybrid models combining the best of both domains. Future work could delve into optimizing the inference process to decrease latency, potentially borrowing from recent advancements in efficient sampling techniques for diffusion models.

In summary, the DiffusionDet framework represents an interesting shift in the object detection landscape, combining flexibility with robust detection capabilities. The exploration of diffusion models in this context paves the way for more innovative approaches that challenge the conventions of object detection methodologies.