HIRNet: Hypergraph-Induced Iterative Reasoning Network for Crowd Counting

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Abstract
In recent years, point regression-based crowd counting has demonstrated remarkable advantages for accurate counting in highly congested scenes. However, existing single-pass regression frameworks lack an explicit self-correction mechanism, making it difficult to rectify localization shifts or even false positives induced by local visual ambiguity in heavily occluded regions. Moreover, straightforward regression predictions struggle to capture complex cluster-level higher-order relations in dense crowds, limiting the model’s ability to leverage global context to resolve semantic confusion. To address these issues, we reformulate point regression as an iterative optimization process and propose a Hypergraph-induced Iterative Reasoning Network, HIRNet. Specifically, we first design a 2D bidirectional hypergraph iterative reasoning module (HIR-2D), which treats intermediate-round regression points as an explicit structural prior and dynamically constructs bidirectional hypergraphs along the spatial and channel dimensions. By performing higher-order feature aggregation in both dimensions, HIR-2D enforces long-range spatial consistency and recalibrates channel features, thereby effectively suppressing background distractions. Second, to mitigate the matching jitter of regressed point sets across iterations, we introduce an Anchor-Matching Iterative Deep Supervision strategy that locks the bipartite matching from the final round to ensure consistent gradient directions across rounds. Furthermore, we devise a Monotonic Improvement Loss that explicitly constrains the model to progressively reduce geometric errors throughout iterations, stabilizing the reasoning dynamics. Extensive experiments on three mainstream datasets demonstrate that our method substantially outperforms current advanced approaches.
Funding Information
  • National Natural Science Foundation of China (U25A20532)
  • the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (VRLAB2025A01)

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