Diffusion-Guided Region Proposal Network (DGRPN)

• DGRPN introduces diffusion-driven semantic attention and Gaussian-based localization to achieve superior person search accuracy.
• The architecture decouples detection and identification by modulating detection-specific feature maps with hierarchical, text-conditioned diffusion cues.
• Empirical results demonstrate that DGRPN outperforms traditional RPNs and Faster R-CNN in challenging scenarios, enhancing recall and precision.

The Diffusion-Guided Region Proposal Network (DGRPN) is an architectural module designed to enhance person localization in person search tasks by leveraging semantic priors and hierarchical spatial features from pre-trained diffusion models. Originating within the DiffPS framework, DGRPN innovates upon classical region proposal networks by incorporating diffusion-generated attention maps and Gaussian-based localization, resulting in significant accuracy and robustness gains, particularly in complex, cluttered, or occluded environments (Kim et al., 2 Oct 2025).

1. Foundational Principles and Motivation

DGRPN addresses predominant challenges in person search, including the limitations of shared backbone networks for detection and re-identification and the suboptimal spatial sensitivity of conventional RPNs. Traditional frameworks rely on ImageNet pre-trained CNN backbones, applying the same feature representations to both localization and identification, which leads to conflicting optimization objectives. DGRPN instead exploits the semantic richness and structural fidelity of representations encoded by diffusion models, specifically by utilizing their cross-attention mechanisms conditioned on “person” tokens. This enables the network to highlight, localize, and refine candidate person regions with greater context awareness and improved resilience to visual ambiguity and occlusion.

2. Architectural Design and Operational Workflow

DGRPN is built around the extraction and refinement of spatial attention from a frozen diffusion UNet backbone:

• A detection-specific feature map, $F_2$, is extracted from the diffusion model's mid-level feature hierarchy.
• A cross-attention map, denoted as $\text{Mat}_t$, is computed using a “person” text token embedding, obtained via a CLIP text encoder. This attention map localizes regions corresponding to person instances with semantic alignment.
• A hard thresholding operation defines:

$$M_{\text{th}}(i,j) = \begin{cases} \text{Mat}_t(i,j) & \text{if } \text{Mat}_t(i,j) > T \ 0 & \text{otherwise} \end{cases}$$

where $T$ is a predefined or learnable parameter to suppress background and low-confidence activations.

• The nonzero locations $(i,j)$ in $M_{\text{th}}$ define candidate centers for Gaussian kernels. For each such center $(c_x, c_y)$, local spatial statistics set the Gaussian spread, $(s_x, s_y)$, and a kernel is generated:

$$G_k(i,j) = \exp\left( -\frac{(i-c_x)^2}{B(s_x)^2} - \frac{(j-c_y)^2}{B(s_y)^2} \right)$$

with $B(\cdot)$ as a scaling function.

• The final detection map aggregates all candidate kernels via element-wise maximum:

$G_{\text{det}}(i,j) = \max_k G_k(i,j)$

• The detection-specific feature map $F_2$ is modulated using $G_{\text{det}}$:

$$F_{\text{det}} = F_2 + \gamma (G_{\text{det}} \odot F_2)$$

where $\gamma$ is a learnable parameter, and $\odot$ denotes element-wise multiplication.

• These modulated features are input to the bounding box and objectness heads to produce region proposals.

This design ensures that candidate proposals are aligned with both spatial and semantic priors from diffusion attention, enhancing the network’s selective focus and precision in challenging scenes.

3. Diffusion Model Integration and Feature Characteristics

The efficacy of DGRPN is directly tied to the properties of the underlying diffusion backbone. Diffusion models, especially those trained on joint text-image tasks (e.g., Stable Diffusion v2-1), encode spatially rich, multi-scale features along with semantic cross-attention aligned to linguistic queries. The attention maps prompted by a “person” token delineate person regions irrespective of appearance variation or background clutter. This semantic-textual guidance, unavailable in standard RPNs, improves both recall and precision in candidate generation.

Furthermore, the hierarchical diffusion features supply fine-grained detail via iterative denoising, enabling DGRPN to distinguish subtle person cues (such as partial occlusions, pose variance, and small-scale instances) that often confound purely convolutional approaches.

4. Comparative Effectiveness and Empirical Benefits

Empirical evaluations in the DiffPS framework demonstrate that the DGRPN-based detection branch outperforms standard RPNs and Faster R-CNN detectors in both recall and average precision across multiple person search benchmarks:

Method	Recall (%)	AP (%)	mAP/Top-1 (CUHK-SYSU)
DGRPN (DiffPS)	~98.1	~94.8	~98.4 / ~98.8
Faster R-CNN	lower	lower	lower

Notably, DGRPN’s robustness is pronounced in scenarios with occlusion and small person instances, where attention-derived proposals and Gaussian aggregation mitigate missed detections. The decoupled design—freezing the diffusion backbone, extracting task-specific features using DGRPN, and modulating with Gaussian maps—prevents gradient interference and optimally separates localization and identification branches. This modular separation enables higher downstream re-ID performance when paired with supporting modules (MSFRN and SFAN).

5. Applications and Broader Implications

DGRPN’s architecture renders it suitable for real-world deployments in surveillance, public safety, and smart city infrastructure, particularly for person localization in dense, cluttered, or visually complex environments. Its ability to exploit diffusion model attention maps for candidate proposal generation demonstrates that generative model priors can be harnessed for discriminative tasks without retraining or fine-tuning the backbone itself. This modular strategy indicates the potential of cross-modal, text-conditional attention for robust computer vision beyond person search, suggesting broader applications in multi-object detection and cross-domain tasks.

A plausible implication is that future region proposal networks may increasingly leverage frozen generative backbones with task-specific attention or prompt-guided feature modulation, circumventing the limitations of conventional CNNs and enabling greater flexibility in subsequent instance-specific modules.

6. Limitations and Outlook

While DGRPN yields clear advantages in localization, it requires access to high-quality diffusion model attention maps and significant computational resources for multi-head attention extraction, particularly for large-scale or real-time scenarios. As only the detection-specific branch is modulated by the DGRPN proposals, the overall accuracy of the re-ID pipeline may remain sensitive to the quality of the initial attention prompt, although this is partly ameliorated via downstream refinement modules.

Future directions include optimizing the efficiency of attention extraction, extending to broader object categories through richer prompt libraries, and integrating dynamically learned thresholds and aggregation scales. The overall approach of decoupling tasks and leveraging frozen generative model backbones may influence the design of object detection systems beyond the person search domain.

7. Related Developments and Position within the Field

The development of DGRPN and the associated DiffPS framework establishes a methodological precedent for the integration of generative priors into discriminative detection systems. It aligns with a broader trend toward leveraging pre-trained, text-conditional diffusion models in downstream vision tasks, offering a pathway for incorporating explicit semantic guidance and hierarchical feature sets. This stands in contrast to methods that solely rely on pre-trained discriminative models or shared-parameter multi-tasking, outlining a paradigm shift toward task decoupling and attention-based proposal generation using cross-modal supervision (Kim et al., 2 Oct 2025).