MTCNET: 用于人群计数估计的多任务学习范式
MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation
AI总结：
针对人群计数因尺度变化和视角问题的挑战，提出MTCNet多任务学习架构，以密度估计为主任务、计数组分类为辅助任务，共享VGG-16前端，在ShanghaiTech和UCF_CC_50数据集上MAE分别降低5.8%、14.9%和10.5%。
摘要：
我们提出了一种基于多任务学习（MTL）范式的深度神经网络架构，称为MTCNet（多任务人群网络），用于人群密度和计数估计。由于个体图像存在非均匀尺度变化和任意视角，人群计数估计具有挑战性。所提出的模型包含两个相关任务，以人群密度估计为主任务，人群计数组分类为辅助任务。辅助任务有助于捕捉相关的尺度信息，从而提高主任务的性能。主任务模型包括两个模块：用于特征提取的VGG-16前端和用于密度图生成的扩张卷积神经网络。辅助任务模型与主任务共享相同的前端，其后是一个CNN分类器。在不使用任何数据增强的情况下，我们提出的网络在ShanghaiTech数据集上的平均绝对误差（MAE）比最先进的方法低5.8%和14.9%。我们的模型在UCF_CC_50数据集上也表现更优，MAE降低了10.5%。
英文摘要：
We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model has two related tasks, with Crowd Density Estimation as the main task and Crowd-Count Group Classification as the auxiliary task. The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task. The main task model comprises two blocks: VGG-16 front-end for feature extraction and a dilated Convolutional Neural Network for density map generation. The auxiliary task model shares the same front-end as the main task, followed by a CNN classifier. Our proposed network achieves 5.8% and 14.9% lower Mean Absolute Error (MAE) than the state-of-the-art methods on ShanghaiTech dataset without using any data augmentation. Our model also outperforms with 10.5% lower MAE on UCF_CC_50 dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
