Abstract
Person re-identification (ReID) is an extremely important area in both surveillance and mobile applications, requiring strong accuracy with minimal computational cost. State-of-the-art methods give good accuracy but with high computational budgets. To remedy this, this paper proposes VisNet, a computationally efficient and effective re-identification model suitable for real-world scenarios. It is the culmination of conceptual contributions, including feature fusion at multiple scales with automatic attention on each, semantic clustering with anatomical body partitioning, a dynamic weight averaging technique to balance classification semantic regularization, and the use of loss function FIDI for improved metric learning tasks. The multiple scales fuse ResNet50's stages 1 through 4 without the use of parallel paths, with semantic clustering introducing spatial constraints through the use of rule-based pseudo-labeling. VisNet achieves 87.05% Rank-1 and 77.65% mAP on the Market-1501 dataset, having 32.41M parameters and 4.601 GFLOPs, hence, proposing a practical approach for real-time deployment in surveillance and mobile applications where computational resources are limited.
Abstract (translated)
人员再识别（ReID）在监控和移动应用领域是非常重要的一个方面，它需要在计算成本极低的情况下保持高精度。虽然最先进的方法提供了很好的准确率，但它们的计算开销较大。为解决这一问题，本文提出了VisNet，这是一种计算效率高且有效的人体再识别模型，适用于现实场景。该模型集成了多个概念贡献，包括多尺度特征融合和自动注意力机制、基于解剖学身体部位划分的语义聚类、用于平衡分类语义正则化的动态权重平均技术以及利用损失函数FIDI改进度量学习任务。 VisNet在不使用并行路径的情况下融合了ResNet50第1至4阶段的特征，通过规则基础伪标签引入空间约束。实验表明，在Market-1501数据集上，VisNet实现了87.05%的Rank-1准确率和77.65%的mAP（平均精度），并且参数量为32.41M，计算复杂度为4.601 GFLOPs。因此，VisNet提出了一种在监控和移动应用中具有实际意义的方法，在这些场景下计算资源有限的情况下仍可实现实时部署。
URL
https://arxiv.org/abs/2601.00307
