Foggy Crowd Counting: Combining Physical Priors and KAN-Graph

• URL: http://arxiv.org/abs/2509.24545v1
• Date: Mon, 29 Sep 2025 09:59:36 GMT
• Title: Foggy Crowd Counting: Combining Physical Priors and KAN-Graph
• Authors: Yuhao Wang, Zhuoran Zheng, Han Hu, Dianjie Lu, Guijuan Zhang, Chen Lyu,
• Abstract summary: This paper proposes a crowd-counting method with a physical a priori of atmospheric scattering.<n>It achieves a 12.2%-27.5% reduction in MAE metrics compared to mainstream algorithms in dense fog scenarios.
• Score: 19.690003059392893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aiming at the key challenges of crowd counting in foggy environments, such as long-range target blurring, local feature degradation, and image contrast attenuation, this paper proposes a crowd-counting method with a physical a priori of atmospheric scattering, which improves crowd counting accuracy under complex meteorological conditions through the synergistic optimization of the physical mechanism and data-driven.Specifically, first, the method introduces a differentiable atmospheric scattering model and employs transmittance dynamic estimation and scattering parameter adaptive calibration techniques to accurately quantify the nonlinear attenuation laws of haze on targets with different depths of field.Secondly, the MSA-KAN was designed based on the Kolmogorov-Arnold Representation Theorem to construct a learnable edge activation function. By integrating a multi-layer progressive architecture with adaptive skip connections, it significantly enhances the model's nonlinear representation capability in feature-degraded regions, effectively suppressing feature confusion under fog interference.Finally, we further propose a weather-aware GCN that dynamically constructs spatial adjacency matrices using deep features extracted by MSA-KAN. Experiments on four public datasets demonstrate that our method achieves a 12.2\%-27.5\% reduction in MAE metrics compared to mainstream algorithms in dense fog scenarios.

Related papers

• Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage [65.51149575007149]
  We present Fun-DDPS, a generative framework that combines function-space diffusion models with differentiable neural operator surrogates for both forward and inverse modeling.<n>Fun-DDPS produces physically consistent realizations free from the high-frequency artifacts observed in joint-state baselines.
  arXiv  Detail & Related papers  (2026-02-12T18:58:12Z)
• Characterizing and Optimizing the Spatial Kernel of Multi Resolution Hash Encodings [2.497913938263034]
  Multi-Resolution Hash Function (MHE) provides a powerful parameterization for neural fields.<n>This work introduces a novel analytical approach that characterizes MHE by examining its Point Spread (PSF)<n>We demonstrate how collisions introduce speckle noise and degrade the Signal-to-Noise Ratio (SNR)
  arXiv  Detail & Related papers  (2026-02-11T03:55:42Z)
• Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization [6.097829741846035]
  EmbedOpt is an inference-time approach for steering diffusion models to optimize experimental likelihoods in the conditional embedding space.<n>We show that EmbedOpt outperforms the coordinate-based posterior sampling method in map fitting tasks and matches performance on distance constraint tasks.
  arXiv  Detail & Related papers  (2026-02-05T04:13:33Z)
• HAD: Hierarchical Asymmetric Distillation to Bridge Spatio-Temporal Gaps in Event-Based Object Tracking [80.07224739976911]
  Event cameras offer exceptional temporal resolution and a range (modal)<n> RGB cameras excel at capturing rich texture with high resolution, whereas event cameras offer exceptional temporal resolution and a range (modal)
  arXiv  Detail & Related papers  (2025-10-22T13:15:13Z)
• Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulation [5.1767204510818985]
  Single image super-resolution traditionally assumes spatially-invariant degradation models.<n>Real-world imaging systems exhibit complex distance-dependent effects including atmospheric scattering, depth-of-field variations, and perspective distortions.<n>We propose a rigorous variational framework that characterizes super-resolution as a spatially-varying inverse problem.
  arXiv  Detail & Related papers  (2025-09-06T15:35:37Z)
• Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection [53.137651284042434]
  Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples limits the effectiveness of existing methods.<n>We propose Generate grained Anomaly (GAA), a region-guided, few-shot anomaly image-mask pair generation framework.<n>GAA generates realistic, diverse, and semantically aligned anomalies using only a small number of samples.
  arXiv  Detail & Related papers  (2025-07-13T12:56:59Z)
• Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations [0.0]
  This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields.<n>The proposed approach, MARIO, addresses non parametric geometric variability through an efficient shape encoding mechanism.<n>It enables training on significantly downsampled meshes, while maintaining consistent accuracy during full-resolution inference.
  arXiv  Detail & Related papers  (2025-05-14T10:49:08Z)
• DA-STGCN: 4D Trajectory Prediction Based on Spatiotemporal Feature Extraction [6.781509470656284]
  We propose DA-STGCN, an innovative graph convolutional network that integrates a dual attention mechanism.<n>Our model reconstructs adjacency matrix through a self-attention approach, enhancing the capture of node correlations.<n>Results demonstrate a notable improvement over current 4D trajectory prediction methods, achieving a 20% and 30% reduction in the Average Displacement Error (Attention) and Final Displacement Error (FDE)
  arXiv  Detail & Related papers  (2025-03-05T03:42:49Z)
• Multi-Modality Driven LoRA for Adverse Condition Depth Estimation [61.525312117638116]
  We propose Multi-Modality Driven LoRA (MMD-LoRA) for Adverse Condition Depth Estimation.<n>It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning (VTCCL)<n>It achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets.
  arXiv  Detail & Related papers  (2024-12-28T14:23:58Z)
• FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation [8.78717459496649]
  We propose FlowDepth, where a Dynamic Motion Flow Module (DMFM) decouples the optical flow by a mechanism-based approach and warps the dynamic regions thus solving the mismatch problem. For the unfairness of photometric errors caused by high-freq and low-texture regions, we use Depth-Cue-Aware Blur (DCABlur) and Cost-Volume sparsity loss respectively at the input and the loss level to solve the problem.
  arXiv  Detail & Related papers  (2024-03-28T10:31:23Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models [72.93652777646233]
  Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. We propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask.
  arXiv  Detail & Related papers  (2023-05-29T07:49:44Z)
• Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso [102.84915019938413]
  Non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG) offer promise of non-invasive techniques. The problem of source localization, or source imaging, poses however a high-dimensional statistical inference challenge. We propose an ensemble of desparsified multi-task Lasso (ecd-MTLasso) to deal with this problem.
  arXiv  Detail & Related papers  (2020-09-29T21:17:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.