用多头注意力融合CNN与Transformer特征，提升牛脸识别准确率
MHAFF: Multi-Head Attention Feature Fusion of CNN and Transformer for Cattle Identification
- 设计多头注意力机制融合两类特征，捕捉跨模态关系
- 在两个公开数据集上达99.88%和99.52%准确率，优于传统融合方法
- 适合需要高精度牲畜身份识别的农业智能系统应用
卷积神经网络（CNN）已被广泛用于基于鼻部图像的牛只识别，但难以捕捉复杂鼻部图案中的长程依赖关系。而变压器模型能有效处理此类问题。受此启发，本文首次提出多头注意力特征融合（MHAFF）方法，用于融合CNN与Transformer的特征优势。传统融合方式如加法和拼接存在缺陷：加法会丢失判别性信息，拼接则增加维度且无法发现特征间交互。MHAFF通过注意力机制建模不同特征间的关联，同时保留原始特征完整性。实验表明，MHAFF在两个公开牛只识别数据集上均显著优于加法、拼接及现有方法，在两组数据中分别达到99.88%和99.52%的最高准确率，并具备快速收敛能力。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) have drawn researchers' attention to identifying cattle using muzzle images. However, CNNs often fail to capture long-range dependencies within the complex patterns of the muzzle. The transformers handle these challenges. This inspired us to fuse the strengths of CNNs and transformers in muzzle-based cattle identification. Addition and concatenation have been the most commonly used techniques for feature fusion. However, addition fails to preserve discriminative information, while concatenation results in an increase in dimensionality. Both methods are simple operations and cannot discover the relationships or interactions between fusing features. This research aims to overcome the issues faced by addition and concatenation. This research introduces a novel approach called Multi-Head Attention Feature Fusion (MHAFF) for the first time in cattle identification. MHAFF captures relations between the different types of fusing features while preserving their originality. The experiments show that MHAFF outperformed addition and concatenation techniques and the existing cattle identification methods in accuracy on two publicly available cattle datasets. MHAFF demonstrates excellent performance and quickly converges to achieve optimum accuracy of 99.88% and 99.52% in two cattle datasets simultaneously.
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