TY  - EJOU
AU  - Wang, Haixia 
AU  - Zhang, Huan 
AU  - Wang, Xiuling 
AU  - Xin, Xule 
AU  - Zhang, Zhiguo 

TI  - HUANNet: A High-Resolution Unified Attention Network for Accurate Counting
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 1
SN  - 1546-2226

AB  - Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision, with applications ranging from crowd counting to various other object counting tasks. To address this, we propose HUANNet (High-Resolution Unified Attention Network), a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework, while optimizing computational distribution across parallel branches. HUANNet introduces three core modules: the High-Resolution Attention Module (HRAM), which enhances feature extraction by optimizing multi-resolution feature fusion; the Unified Multi-Scale Attention Module (UMAM), which integrates spatial, channel, and convolutional kernel information through an attention mechanism applied across multiple levels of the network; and the Grid-Assisted Point Matching Module (GPMM), which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms. Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD, demonstrating the effectiveness and versatility of HUANNet.
KW  - Accurate counting; high-resolution representations; point-to-point matching

DO  - 10.32604/cmc.2025.069340