Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting

• URL: http://arxiv.org/abs/2606.18566v1
• Date: Wed, 17 Jun 2026 00:38:14 GMT
• Title: Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting
• Abstract summary: Existing methods mainly focus on well-lit scenes or rely on single-modality Red-Green-Blue (RGB) representations.<n>We construct three new low-light crowd counting benchmarks, which consist of two synthetic datasets, SHA_Dark and SHB_Dark, and a real-world benchmark LC-Crowd.<n>We propose a Multi-Modal Hyper-Graph Fusion module, which formulates RGB appearance, depth geometry, and edge structure cues as nodes in a unified hyper-graph.<n>We develop a unified Low-Light Counting Network (LCNet) for robust low-light crowd counting
• Score: 11.186278647833902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Crowd counting is a fundamental task in computer vision. However, crowd counting in low-light environments remains largely underexplored, despite its practical importance in the real world. Existing methods mainly focus on well-lit scenes or rely on single-modality Red-Green-Blue (RGB) representations, which often become unreliable under extreme darkness and complex non-uniform illumination. To handle this problem, we construct three new low-light crowd counting benchmarks, which consist of two synthetic datasets, SHA\_Dark and SHB\_Dark, and a real-world benchmark LC-Crowd (Low-light Crowd Dataset). Inspired by Retinex-based physical modeling, we introduce depth and Canny edge cues as complementary geometric and structural priors to enhance the intrinsic reflectance representation under low-light conditions. We propose a Multi-Modal Hyper-Graph Fusion module, which formulates RGB appearance, depth geometry, and edge structure cues as nodes in a unified hyper-graph and explicitly captures their high-order complementary relationships via dynamic hyperedge construction and message passing. Furthermore, to adaptively allocate computation in dense prediction, we propose a Deformable Rectangular Sparse Attention (DRSA) module, which concentrates computation on informative regions through anchor-aware estimation and adaptive rectangular window modeling. Based on these designs, we develop a unified Low-Light Counting Network (LCNet) for robust low-light crowd counting. Extensive experiments on three benchmarks demonstrate that the proposed method achieves the best overall performance against existing state-of-the-art (SOTA) methods. The code is in the supplementary material. The datasets will be made public upon acceptance.

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