# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 3 Method
### Adaptive Denoising  
Incorporating AAFI effectively refined semantic features, potentially improving the efficiency of adaptive noise generation. To further improve efficiency, we optimized the decoder structure. Our decoder inherited its structure from the DiffusionDet model, where multi-scale feature maps and a proposal box set \(P_{n}=\{n_{i}\mid n_{i}\in\mathbb{R}^{N\times 4}\}\) are passed in during the initial stage, with \(N\) denoting the fixed number of proposals (e.g., 100) and \(i=1,2,\dots\), the batch size. Proposal boxes are initialized as noisy boxes, generated by adding Gaussian noise to ground truth boxes. In this paper, we use the term noise to refer to the initial proposal box. Noise interacts with the multi-scale feature maps to extract the ROI features \(R_{i}=\{f_{i}\mid f_{i}\in\mathbb{R}^{7\times 7\times C}\}\), where \(C\) is the number of feature channels, typically set to 256. These ROI features serve as the basis for initializing the proposal features \(P_{f}=\{o_{i}\mid o_{i}\in\mathbb{R}^{C}\}\). The proposal features are then further refined to generate the final prediction outputs.  
Figure 2: Generation pipeline of adaptive noise.  
Specifically, the extracted \(P_{f}\) features undergo a self-attention mechanism, which enhances their feature representation by capturing global context information. Following this, the refined \(P_{f}\) features interact with the ROI features through dynamic convolution, resulting in \(O_{f}\). This output serves as the basis for adjusting the offsets of the proposal boxes and is passed as input to the next decoder layer. The decoder consists of six layers, each layer processing the proposal features independently without sharing parameters. As the proposal features are progressively refined across layers, the offsets for the proposal boxes are iteratively updated. The final prediction outputs, including the adjusted bounding boxes, are generated at the last decoder layer. However, this structure also introduces a drawback, as it significantly increases the size of DiffusionDet, with its learnable parameters typically double that of other models.  
In our model, the use of adaptive noise as the initial proposal box for the decoder input instead of random noise ensures that the initial adaptive noise contains more positional information, making each stage's output closer to the ground truth. Benefiting from this efficient structure, we adopted the strategy of reusing the decoder in multiple sampling iterations from DiffusionDet, without the need to modify the decoder structure to achieve multiple reuses. We reduced the decoder layers to a single layer and maintained the original functionality by reusing this decoder six times.  
During training, DiffusionDet may experience instability due to the large differences in random noise, whereas our model, empowered by the spatial awareness provided by adaptive noise, achieves more stable training. Additionally, the positional information in adaptive noise improves stability, and the random factors in the adaptive noise give our model a comparable generative ability to DiffusionDet, which is another reason we opted to reduce the number of decoder layers.  
This simplified decoder structure allows for the flexible adjustment of sampling and iteration steps without retraining the model. During training, we can reduce the number of decoder iterations to improve the training speed, while during inference, we can increase the number of iterations to enhance prediction accuracy. Moreover, this structure supports multi-step sampling, enabling us to flexibly adjust the number of sampling steps and iterations to balance precision and speed according to different needs.