Event-Guided Diffusion Model

• Event-guided diffusion model is a generative technique that conditions iterative denoising on structured event signals like depth cues and haze parameters.
• It employs specialized mechanisms such as adaptive depth-aware kernels and hybrid event representations to improve robustness under adverse conditions.
• Empirical results from benchmarks like HazyDet demonstrate measurable performance gains with minimal added complexity in real-world scenarios.

An event-guided diffusion model is a class of generative modeling technique designed to synthesize or interpret visual data in which the generative process is conditioned by structured "event" signals—such as depth cues, scene attributes, or environmental parameters—beyond standard pixel-level or semantic labels. While traditional diffusion models iteratively refine samples via a learned denoising operator, event-guided extensions incorporate auxiliary event-driven or scene-sensing information, enabling the models to generate outputs more faithful to complex real-world conditions, e.g., atmospheric haze, depth discontinuities, and adverse scene factors. In modern computer vision, event-guided diffusion is central to robust perception under non-ideal sensor, weather, or environmental regimes.

1. Theoretical Foundations and Motivation

Diffusion models operate by defining a parameterized Markov chain that gradually transforms a simple prior distribution (e.g., Gaussian noise) into complex data distributions. By training a neural denoising model to reverse each noising step, these models have demonstrated state-of-the-art sample quality and theoretical tractability across modalities. However, unconditioned diffusion models often underperform when target distributions are affected by structured phenomena—like weather, depth, or transmission effects—not fully captured by pixel statistics.

Event-guided diffusion extends this paradigm by introducing event signals—quantitative or categorical side channels (e.g., depth maps, visibility, transmission coefficients)—into the model's conditioning space. This conditioning allows the denoising operator to adapt its predictions based on scene or sensor context, leading to improved reconstructions and robustness, particularly for domains exhibiting strong multimodality or domain shift.

2. Conditioning Mechanisms: Event Representation and Integration

Event-guided diffusion models require principled mechanisms for embedding and delivering side information to all relevant stages of the generation process. Typical event representations include:

• Dense per-pixel maps (e.g., metric depth, confidence, or transmission)
• Global or blockwise scene descriptors (e.g., atmospheric scattering parameters)
• Hybrid representations that combine local and global event semantics

Integration may use direct concatenation to feature maps, cross-attention, or dynamic kernel modulation. For example, depth-conditioned inference exploits spatial depth cues to inform convolutional operations at each diffusion step—enabling, for instance, haze-aware restoration guided by the underlying scene geometry.

In the "HazyDet" framework, depth cues obtained via zero-shot monocular depth estimation are encoded and used to dynamically modulate feature representations. Specifically, the Depth-Cue Condition Kernel (DCK) generates spatially-adaptive convolution kernels from estimated depth, which are then applied to the detection feature maps, enabling local adaptation to haze-induced degradation (Feng et al., 30 Sep 2024).

3. Dataset Construction and Event Signal Alignment

Event-guided diffusion models necessitate datasets containing both high-quality event annotations and aligned main data (e.g., RGB images and depth, or transmittance ground truth). Synthetic datasets are often constructed by rendering or perturbing clear real-world images using physical models, parameterized by event variables.

For drone-based object detection under haze, the HazyDet benchmark includes:

• 11,600 images (∼94.8% synthetic with simulated haze, ∼5.2% real-world hazy scenes)
• Aligned depth pseudo-ground-truth from monocular depth networks, refined to address label noise
• Sampling of haze parameters (global atmospheric light $A$, scattering coefficient $\beta$) from truncated normal distributions to match real-scene statistics
• Synthetic hazy observations $I(x, y)$ computed via:

$$I(x, y) = J(x, y) t(x, y) + A (1 - t(x, y)), \quad t(x, y) = e^{-\beta d(x, y)}$$

where $J(x, y)$ is the clear image and $d(x, y)$ the depth (Feng et al., 30 Sep 2024).

This alignment ensures event-guided diffusion models can be trained with ground-truth or strong pseudo-ground-truth event signals, supporting robust conditioning.

4. Event-Guided Diffusion Model Architectures

Event-guided architectures incorporate dedicated modules for event input pathways, feature fusion, and event-adaptive convolution. In the DeCoDet model, the processing stack comprises:

• Backbone feature extraction
• Multi-scale Depth-aware Detection Heads (MDDH) with a dedicated depth regression branch
• Depth-Cue Condition Kernel (DCK) module, which injects depth-modulated spatial kernels into the detection feature maps:

$$\mathcal{H}_{i,j} = \varphi(X_{i,j}) = W_1 \sigma(W_0 X_{i,j})$$

$$Y'_{i,j,k} = \sum_{(u,v)\in\Delta_K} \mathcal{H}_{i,j,u+\lfloor K/2\rfloor,v+\lfloor K/2\rfloor,\lceil kG/C\rceil} \cdot Y_{i+u,j+v,k}$$

where $X_{i,j}$ is depth encoding at location $(i, j)$, $W_0, W_1$ are learned weights, $K$ is kernel size, $G$ is group number, and $Y$ is the detection feature tensor.

Loss functions reflect joint optimization of detection and event-guided (e.g., scale-invariant depth) loss terms. To address noise in depth pseudo-labels, the Scale-Invariant Refurbishment Loss (SIRLoss) is employed:

$$L_\text{Dep}(y, y^*) = \left( \frac{1}{n} \sum d'_i{}^2 - \frac{1}{n^2} (\sum d'_i)^2 \right), \quad d'_i = \log y_i - \log \hat{y}_i, \quad \hat{y}_i = \alpha y^*_i + (1-\alpha) y_i$$

with $\alpha=0.9$, blending noisy labels with network outputs for greater stability (Feng et al., 30 Sep 2024).

5. Benchmark Protocols and Empirical Comparisons

Standardized protocols evaluate the contribution of event-guided diffusion in robust detection. In the HazyDet regime:

• Data splits: synthetic (train/val/test: 8,000/1,000/2,000), real hazy (600 images for test only)
• Metrics: mean Average Precision (mAP) using standard IoU thresholds
• Baseline object detectors (FCOS, VFNet, Cascade R-CNN, Deformable DETR) show significant performance drops in real-world haze, underscoring the necessity of event guidance

The incorporation of depth-guided DCK and SIRLoss in FCOS-DeCoDet yields an absolute mAP gain of +1.5 on synthetic and a comparable improvement on the real subset (24.3 vs. 22.8 mAP), with negligible parameter overhead, indicating practical feasibility. Similar gains are observed for VFNet-based detectors (Feng et al., 30 Sep 2024).

6. Application Domains and Future Directions

Event-guided diffusion is applicable to numerous adverse sensing modalities: haze, rain, snow, low-light, and any regime where scene-readout is modulated by quantifiable events (depth, transmittance, weather). Applications include drone autonomy, robotics, remote sensing, and situational awareness in degraded visual environments.

Best practices highlight the necessity of:

• Realistic event distribution modeling (e.g., truncated normal sampling of haze parameters)
• Strong zero-shot event predictor backbones (e.g., Metric3D for depth)
• Multi-scale event-feature integration (combining global and local signal adaptation)
• Low-complexity event branches to maintain efficiency

Continued challenges include bridging synthetic-to-real domain gaps, improving event signal quality (with superior sensors or depth estimation backbones), and extending frameworks to multi-modal sensor regimes and more complex events (Feng et al., 30 Sep 2024). Event-guided diffusion is expected to accelerate research in robust perception and adaptive generative modeling.