@article{Wang2026, 
author = {Haixia Wang and Huan Zhang and Xiuling Wang and Xule Xin and Zhiguo Zhang},
title = {HUANNet: A High-Resolution Unified Attention Network for Accurate Counting},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {1},
pages = {1-20},
keywords = {Accurate counting, high-resolution representations, point-to-point matching},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069340},
doi = {10.32604/cmc.2025.069340},
abstract = {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.}
}