# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 3 Method
### Adaptive Noise  
While the noise-to-box paradigm simplifies object detection through random noise, this randomness often leads to significant discrepancies between the noise-generated bounding boxes and the ground truth. As a result, the decoder is heavily relied upon to adjust these noise-generated boxes to align with the true values, which increases the computational burden during the denoising process. To address this issue, we introduced an adaptive noise mechanism that incorporates learnable parameters. This approach not only retains the simplicity of random noise but also enhances the model's ability to accurately capture object locations. By effectively reducing the gap between the initial object candidates and the ground truth, this mechanism improves both the accuracy and efficiency of the denoising process. Specifically, we designed a structural framework consisting of two key modules: the Adaptive Attention-Based Feature Interaction (AAFI) module and the Fast Adaptive Pooling (FAP) module. The entire process is expressed by the following equation:  
\[lv=\sigma(\text{FAP}(\text{AAFI}(Q,K,V))) \tag{5}\]  
\[Ada(y_{t})=Concat(y_{t},lv) \tag{6}\]  
where \(lv\) represents the latent code, extracted through AAFI and FAP, followed by the sigmoid activation function \(\sigma\), defined as \(\sigma(x)=\frac{1}{1+x^{-1}}\). The terms \(Q\), \(K\), and \(V\) represent the query, key, and value matrices, which are computed by projecting the input features into different subspaces. These matrices enable the calculation of interactions or relationships between input elements. Finally, \(Ada(y_{t})\) represents the adaptive noise, constructed by combining \(y_{t}\) with \(lv\). AAFI focuses on refining the high-level semantic representation within a single layer of the Feature Pyramid Network (FPN) [40], enabling the network to prioritize the most relevant spatial and semantic information. The FPN, widely used in object detection, generates multi-scale features by combining high-level semantics with fine spatial details, typically outputting four layers, with P5 being the lowest resolution and richest in semantic  
Figure 1: Our model compared with DiffusionDet: (**a**) DiffusionDet, (**b**) DifAda. information. AAFI operates exclusively on the P5 layer, leveraging its global contextual information to improve the representation of high-level concepts. This design reduces computational redundancy by avoiding high-resolution intra-scale interactions, which often introduce ambiguity due to insufficient semantic depth. By focusing on P5, AAFI enhances computational efficiency while strengthening the model's ability to distinguish fine-grained details in complex scenes. To further boost computational efficiency and allow for adaptation to objects of varying scales, we introduced the FAP module, which standardizes output sizes and enhances the model's scalability. FAP adopts a progressive pooling strategy to refine the feature representation of the P5 layer. By performing the pooling operation in three sequential stages, FAP effectively reduces the tensor size while maintaining computational efficiency. This multi-stage pooling strategy allows the module to capture features at multiple scales, enabling the model to better perceive objects of varying sizes within the scene. FAP leverages the output from the previous pooling stage as the input for each subsequent stage, minimizing redundant computational work. This progressive approach not only conserves computational resources but also standardizes the output size of the P5 feature layer. Since our model does not impose a fixed input size constraint, the feature maps generated by the encoder may exhibit variability. Through this strategy, FAP becomes adaptive, ensuring that the output tensor size remains consistent while enhancing the model's ability to detect both small and large objects effectively. This improves both efficiency and accuracy, making FAP a robust addition to our adaptive noise pipeline.