Mengqi Lei

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.

In recent years, crowd counting has garnered increasing attention due to its wide range of societal applications. However, the vast differences in crowd density distributions across various areas within a scene make it difficult for existing crowd counting methods to achieve satisfactory performance. To address this issue, this letter introduces a novel method called Dynamic Density-Region-Aware Network (DDRANet). First, a Density-Aware Module (DAM) is designed to detect density variations and dynamically segment the image into multiple density regions. Subsequently, the Region Information Encoder (RIE) transforms the obtained density region map into a latent space to enhance the information of density regions. Then, through a specially designed Region Attention Block (RAB), the encoded density region map guides the model to focus on different density regions separately, enabling the model to adapt to the density variations of the crowd within the image. Furthermore, we combine Gaussian kernel function and Density Peak Clustering to automatically generate ground truth of density region maps, supervising the optimization of DAM. Extensive experimental results on three datasets demonstrate DDRANet's superior performance over other state-of-the-art methods.

Small object detection in remote-sensing images is essential, yet challenging due to the unique characteristics of small objects. On the one hand, it can be difficult to distinguish small objects from complex backgrounds. On the other hand, due to their minuscule size, small objects are also easily submerged during feature fusion. This letter proposes a novel detection method called sparse outlook network (SOLO-Net) to address these issues. First, we propose a top-k sparse outlook (TKSO) attention module and the sparse outlook path aggregation network (SOLO-PAN) as the fundamental component of SOLO-Net. This module improves the performance of the path aggregation network (PANet), thereby enhancing the ability of the model to focus on small objects. Second, we propose a Sigmoid-intersection over union (IoU) loss function specifically designed for small objects, accelerating model convergence and improving detection performance. Finally, we evaluate our model on the RSOD and DIOR datasets, achieving mean average precision (mAP) scores of 93.8% and 74.5%, respectively.